14.2.7 Distance Measures, Criteria for Clustering

Butler, G.A.,
A vector field approach to cluster analysis,
PR(1), No. 4, July 1969, pp. 291-299.
Elsevier DOI 0309
Use an extension of the gravitational field to find clusters of multidimensional data. BibRef

Girard, A.L., Lowenstein, F.,
On the applicability of modern algebra techniques to adaptive pattern recognition,
PR(2), No. 1, January 1970, pp. 33-38.
Elsevier DOI 0309
Mappings to encode pattern classes into a linear vector space. BibRef

Henrichon, E.G., and Fu, K.S.,
A Nonparametric Partitioning Procedure for Pattern Classification,
TC(18), July 1969, pp. 615-623. BibRef 6907

Owen, J.[Joel], Brick, D.B.[Donald B.], Henrichon, E.G.[Ernest G.],
A nonparametric approach to pattern recognition,
PR(2), No. 3, September 1970, pp. 227-234.
Elsevier DOI 0309
Transform the underlying unknown probability structure of a pattern discrimination problem to the real line. BibRef

Anderson, D.N., Isenhour, T.L.,
Empirical studies of separability and prediction using threshold logic units,
PR(5), No. 3, September 1973, pp. 249-257.
Elsevier DOI 0309
BibRef

Moore, D.S.[David S.], Whitsitt, S.J.[Stephen J.], and Landgrebe, D.A.[David A.],
Variance Comparisons for Unbiased Estimators of Probabilities of Correct Classifications,
IT(22), No. 1, January 1976, pp. 102-105. BibRef 7601

Al-Alaoui, M.A.[M. Adnan],
Application of constrained generalized inverse to pattern classification,
PR(8), No. 4, October 1976, pp. 277-281.
Elsevier DOI 0309
Adaptive procedure, classify the means of the classes correctly and then vary the margin of this classification to achieve the least possible errors on the design set. BibRef

Porter, W.A.[William A.],
A class of error tolerant pattern discrimination functions,
PR(9), No. 1, January 1977, pp. 59-64.
Elsevier DOI 0309
BibRef

Porter, W.A.[William A.],
A comparison of selected pattern recognition functions,
PR(9), No. 2, July 1977, pp. 77-87.
Elsevier DOI 0309
BibRef

Porter, W.A.[William A.],
Synthesising cluster operators,
PR(11), No. 2, 1979, pp. 127-132.
Elsevier DOI 0309
BibRef

Haralick, R.M.[Robert M.],
Pattern discrimination using ellipsoidally symmetric multivariate density functions,
PR(9), No. 2, July 1977, pp. 89-94.
Elsevier DOI 0309
BibRef

Ben-Bassat, M.[Moshe], Gal, S.[Shmuel],
Properties and convergence of a posteriori probabilities in classification problems,
PR(9), No. 2, July 1977, pp. 99-107.
Elsevier DOI 0309
BibRef

Wright, W.E.,
Gravitational clustering,
PR(9), No. 3, October 1977, pp. 151-166.
Elsevier DOI 0309
BibRef

Holgersson, M.[Margareta],
The limited value of cophenetic correlation as a clustering criterion,
PR(10), No. 4, 1978, pp. 287-295.
Elsevier DOI 0309
BibRef

Turner, J.C.[John C.], Tsokos, C.P.[Chris P.],
A nonparametric classification scheme with mean squared error criterion,
PR(10), No. 1, 1978, pp. 47-53.
Elsevier DOI 0309
BibRef

Lapsa, P.M.[Paul M.],
Some statistical bounds for the accuracy of distance-based pattern classification,
PR(11), No. 2, 1979, pp. 95-108.
Elsevier DOI 0309
BibRef

Tremolieres, R.[Raymond],
The percolation method for an efficient grouping of data,
PR(11), No. 4, 1979, pp. 255-262.
Elsevier DOI 0309
BibRef

Föglein, J., Kittler, J.V.,
The Effect of Pixel Correlations on Class Separability,
PRL(1), 1983, pp. 401-407. BibRef 8300

Day, W.H.E., and Wells, R.S.,
Extremes in the Complexity of Computing Metric Distance Between Partitions,
PAMI(6), No. 1, January 1984, pp. 69-73. BibRef 8401

Smith, S.P., and Jain, A.K.,
Testing for Uniformity in Multidimensional Data,
PAMI(6), No. 1, January 1984, pp. 73-81. BibRef 8401

Smith, S.P., and Jain, A.K.,
A Test to Determine the Multivariate Normality of a Data Set,
PAMI(10), No. 5, September 1988, pp. 757-761.
IEEE DOI BibRef 8809

Ito, T., Kodama, Y., and Toyoda, J.,
A Similarity Measure Between Patterns with Nonindependent Attributes,
PAMI(6), No. 1, January 1984, pp. 111-115. BibRef 8401

Lasry, M.J., and Stern, R.M.,
A Posteriori Estimation of Correlated Jointly Gaussian Mean Vectors,
PAMI(6), No. 4, July 1984, pp. 530-535. BibRef 8407

Bailey, T., and Cowles, J.,
Cluster Definition by the Optimization of Simple Measures,
PAMI(6), No. 5, September 1984, pp. 645-652. BibRef 8409

Therrien, C.W., and Fukunaga, K.,
Properties of Separable Covariance Matrices and Their Associated Gaussian Random Processes,
PAMI(6), No. 5, September 1984, pp. 651-656. BibRef 8409

Takiyama, R.,
The Separating Capacity of a Multithreshold Threshold Element,
PAMI(7), No. 1, January 1985, pp. 112-116. BibRef 8501

Krueger, F.R.,
Comments on Takiyama's Analysis of the Multithreshold Threshold Element,
PAMI(8), No. 6, November 1986, pp. 760-761. BibRef 8611

Duchene, J.,
A new form of discriminant surfaces using polar coordinates,
PR(20), No. 4, 1987, pp. 437-442.
Elsevier DOI 0309
BibRef

Young, D.M.[Dean M.], Turner, D.W.[Danny W.], Marco, V.R.[Virgil R.],
On the robustness of the equal-mean discrimination rule with uniform covariance structure against serially correlated training data,
PR(21), No. 2, 1988, pp. 189-194.
Elsevier DOI 0309
BibRef

Danilowicz, C.[Czeslaw], Nguyen, N.T.[Ngoc Thanh],
Consensus-based partitions in the space of ordered partitions,
PR(21), No. 3, 1988, pp. 269-273.
Elsevier DOI 0309
A partition to which the sum of distance from fixed partitions is minimal. BibRef

di Nola, A., Pedrycz, W.[Witold], Sessa, S., Sanchez, E.,
Designing of classification procedures with the use of equality and difference operators,
PR(23), No. 8, 1990, pp. 911-917.
Elsevier DOI 0401
BibRef
And: A2 only:
Author's reply to comments,
PR(25), No. 9, September 1992, pp. 1071-1072.
Elsevier DOI 0401

See also critical comment on the paper designing of classification procedures with the use of equality and difference operators, A. BibRef

Hirota, K., Pedrycz, W.,
Prototype construction and evaluation as inverse problems in pattern classification,
PR(25), No. 6, June 1992, pp. 601-608.
Elsevier DOI 0401
measuring a distance between a pattern to be classified and some prototypes of classes. BibRef

Kuncheva, L.I.[Ludmila I.],
A critical comment on the paper 'designing of classification procedures with the use of equality and difference operators',
PR(25), No. 9, September 1992, pp. 1069-1071.
Elsevier DOI 0401

See also Designing of classification procedures with the use of equality and difference operators. BibRef

Chou, P.A.,
Optimal Partitioning for Classification and Regression Trees,
PAMI(13), No. 4, April 1991, pp. 340-354.
IEEE DOI BibRef 9104

Esposito, F., Malerba, D., and Semeraro, G.,
Classification in Noisy Environments Using a Distance Measure Between Structural Symbolic Descriptions,
PAMI(14), No. 3, March 1992, pp. 390-402.
IEEE DOI Applied to document analysis. BibRef 9203

Valiveti, R.S., Oommen, B.J.,
On using the chi-squared metric for determining stochastic dependence,
PR(25), No. 11, November 1992, pp. 1389-1400.
Elsevier DOI 0401
BibRef

Valiveti, R.S., Oommen, B.J.,
Determining stochastic dependence for normally distributed vectors using the chi-squared metric,
PR(26), No. 6, June 1993, pp. 975-987.
Elsevier DOI 0401
BibRef

Shen, H.C., Bie, C.Y.C., Chiu, D.K.Y.,
A Texture-Based Distance Measure For Classification,
PR(26), No. 9, September 1993, pp. 1429-1437.
Elsevier DOI BibRef 9309

Boberg, J.[Jorma], Salakoski, T.[Tapio],
General formulation and evaluation of agglomerative clustering methods with metric and non-metric distances,
PR(26), No. 9, September 1993, pp. 1395-1406.
Elsevier DOI 0401
BibRef

Dubuisson, B.[Bernard], Masson, M.[Mylène],
A statistical decision rule with incomplete knowledge about classes,
PR(26), No. 1, January 1993, pp. 155-165.
Elsevier DOI 0401
BibRef

Banerjee, S.[Saibal], Rosenfeld, A.[Azriel],
Model-based cluster analysis,
PR(26), No. 6, June 1993, pp. 963-974.
Elsevier DOI 0401
BibRef

Vernazza, G.[Gianni],
Image classification by extended certainty factors,
PR(26), No. 11, November 1993, pp. 1683-1694.
Elsevier DOI 0401
BibRef

Yan, B.[Bolin],
Semiconormed possibility integrals and multi-feature pattern classification,
PR(26), No. 12, December 1993, pp. 1855-1862.
Elsevier DOI 0401
a nonlinear integration of a function and its corresponding non-normalized possibility measures over feature space. The function of an object's feature vector represents the possibilities with uncertainty that the object belongs to a class. BibRef

Hero, A., and Fessler, J.A.,
A recursive algorithm for computing Cramer-Rao-type bounds on estimator covariance,
IT(40), 1994, pp. 1205-1210. BibRef 9400

Vijaya Kumar, B.V.K., Brasher, J.D., Hester, C.F., Srinivasan, G., Bollapragada, S.,
Synthetic Discriminant Functions for Recognition of Images on the Boundary of the Convex-Hull of the Training Set,
PR(27), No. 4, April 1994, pp. 543-548.
Elsevier DOI BibRef 9404

Gowda, K.C.[K. Chidananda], Ravi, T.V.,
Divisive Clustering of Symbolic Objects Using the Concepts of Both Similarity and Dissimilarity,
PR(28), No. 8, August 1995, pp. 1277-1282.
Elsevier DOI BibRef 9508

Gowda, K.C.[K. Chidananda], Ravi, T.V.,
Agglomerative Clustering of Symbolic Objects Using the Concepts of Both Similarity and Dissimilarity,
PRL(16), No. 6, June 1995, pp. 647-652. BibRef 9506

Gowda, K.C.[K. Chidananda], Krishna, G.,
Agglomerative clustering using the concept of mutual nearest neighbourhood,
PR(10), No. 2, 1978, pp. 105-112.
Elsevier DOI 0309
BibRef

Gowda, K.C.[K. Chidananda], Diday, E.,
Symbolic clustering using a new dissimilarity measure,
PR(24), No. 6, 1991, pp. 567-578.
Elsevier DOI 0401
BibRef

Sudhanva, D., Gowda, K.C.[K. Chidananda],
Dimensionality reduction using geometric projections: A new technique,
PR(25), No. 8, August 1992, pp. 809-817.
Elsevier DOI 0401
BibRef

Tsumura, N.[Norimichi], Itoh, K.[Kazuyoshi], Ichioka, Y.[Yoshiki],
Reliable classification by double hyperspheres in pattern vector space,
PR(28), No. 10, October 1995, pp. 1621-1626.
Elsevier DOI 0401
BibRef

Gupta, L.[Lalit], Tammana, R.[Ravi],
A discrepancy measure for improved clustering,
PR(28), No. 10, October 1995, pp. 1627-1634.
Elsevier DOI 0401
BibRef

Kohn, A.F.[André F.], Nakano, L.G.M.[Luis G.M.], Silva, M.O.E.[Miguel Oliveira E.],
A Class Discriminability Measure Based on Feature Space Partitioning,
PR(29), No. 5, May 1996, pp. 873-887.
Elsevier DOI 9605
BibRef

Darger, D., Stein, C.,
A New Approach to the Minimum Cut Problem,
JACM(43), 1996, pp. 601-640. resampling approach for graph partitioning. Contraction algorithm. BibRef 9600

Rahman, A.F.R., Fairhurst, M.C.,
Selective Partition Algorithm for Finding Regions of Maximum Pairwise Dissimilarity among Statistical Class Models,
PRL(18), No. 7, July 1997, pp. 605-611. 9711
BibRef

Abdel Wahab, H.M.S., Fairhurst, M.C.,
An analysis of discriminatory mechanisms in frequency-weighted memory array pattern classifiers,
PR(22), No. 4, 1989, pp. 411-421.
Elsevier DOI 0309
BibRef

Horiuchi, T.[Takahiko],
Class Selective Rejection Rule to Minimize the Maximum Distance Between Selected Classes,
PR(31), No. 10, October 1998, pp. 1579-1588.
Elsevier DOI 9808
BibRef

Baram, Y.[Yoram],
Partial Classification: The Benefit of Deferred Decision,
PAMI(20), No. 8, August 1998, pp. 769-776.
IEEE DOI You don't have to classify everything, there are things between classes that can be undecided. BibRef 9808

Baram, Y.,
Partial Classification Can Be Beneficial Even for Ideal Separation,
PAMI(20), No. 10, October 1998, pp. 1117.
IEEE DOI BibRef 9810

Ho, T.K.[Tin Kam], Baird, H.S.[Henry S.],
Pattern Classification with Compact Distribution Maps,
CVIU(70), No. 1, April 1998, pp. 101-110.
DOI Link BibRef 9804

Ho, T.K.[Tin Kam], Baird, H.S.,
Estimating the intrinsic difficulty of a recognition problem,
ICPR94(B:178-183).
IEEE DOI 9410
BibRef

Ambroise, C., Govaert, G.,
Convergence of an EM-type algorithm for spatial clustering,
PRL(19), No. 10, 31 August 1998, pp. 919-927. BibRef 9808

Fatemi-Ghomi, N., Palmer, P.L., Petrou, M.,
The Two-Point Correlation Function: A Measure of Interclass Separability,
JMIV(10), No. 1, January 1999, pp. 7-25.
DOI Link BibRef 9901

Kundu, S.[Sukhamay],
Gravitational clustering: a new approach based on the spatial distribution of the points,
PR(32), No. 7, July 1999, pp. 1149-1160.
Elsevier DOI BibRef 9907

Jorgensen, T.M., Linneberg, C.,
Theoretical Analysis and Improved Decision Criteria for the n-Tuple Classifier,
PAMI(21), No. 4, April 1999, pp. 336-347.
IEEE DOI BibRef 9904

Jeon, B., Landgrebe, D.A.,
Partially Supervised Classification Using Weighted Unsupervised Clustering,
GeoRS(37), No. 2, March 1999, pp. 1073.
IEEE Top Reference. BibRef 9903

Fagin, R.[Ronald], Stockmeyer, L.[Larry],
Relaxing the Triangle Inequality in Pattern Matching,
IJCV(30), No. 3, December 1998, pp. 219-231.
DOI Link Distance measures. Image indexing. BibRef 9812

Puzicha, J.[Jan], Hofmann, T.[Thomas], Buhmann, J.M.[Joachim M.],
A theory of proximity based clustering: structure detection by optimization,
PR(33), No. 4, April 2000, pp. 617-634.
Elsevier DOI 0002
BibRef

Roth, V.[Volker], Laub, J.[Julian], Kawanabe, M.[Motoaki], Buhmann, J.M.[Joachim M.],
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data,
PAMI(25), No. 12, December 2003, pp. 1540-1551.
IEEE Abstract. 0401
Clustering on pairwise data, not vectors. BibRef

Fischer, B.[Bernd], Buhmann, J.M.[Joachim M.],
Bagging for path-based clustering,
PAMI(25), No. 11, November 2003, pp. 1411-1415.
IEEE Abstract. 0311
Resampling scheme for clustering, similar to bootstrap aggregation (bagging). Extract long clusters from the data.
See also Path-based clustering for grouping of smooth curves and texture segmentation. BibRef

Hansen, P.[Pierre], Mladenovi, N.[Nenad],
J-MEANS: A New Local Search Heuristic for Minimum Sum of Squares Clustering,
PR(34), No. 2, February 2001, pp. 405-413.
Elsevier DOI 0011
BibRef

Gdalyahu, Y.[Yoram], Weinshall, D.[Daphna], Werman, M.[Michael],
Self-Organization in Vision: Stochastic Clustering for Image Segmentation, Perceptual Grouping, and Image Database Organization,
PAMI(23), No. 10, October 2001, pp. 1053-1074.
IEEE DOI 0110
BibRef
Earlier:
Stochastic Image Segmentation by Typical Cuts,
CVPR99(II: 596-601).
IEEE DOI BibRef
And:
Randomized Algorithm for Pairwise Clustering,
DARPA98(203-206). Clustering as a graph partitioning problem. Apply to segmentation, grouping and image retrieval. BibRef

Gdalyahu, Y.[Yoram], Shental, N.[Noam], Weinshall, D.[Daphna],
Perceptual Grouping and Segmentation by Stochastic Clustering,
CVPR00(I: 367-374).
IEEE DOI 0005
BibRef

Pekalska, E.[Elÿzbieta], Duin, R.P.W.[Robert P.W.],
Dissimilarity representations allow for building good classifiers,
PRL(23), No. 8, June 2002, pp. 943-956.
Elsevier DOI 0204
BibRef
Earlier:
Prototype selection for finding efficient representations of dissimilarity data,
ICPR02(III: 37-40).
IEEE DOI 0211
BibRef
Earlier:
Classifiers for Dissimilarity-based Pattern Recognition,
ICPR00(Vol II: 12-16).
IEEE DOI 0009
BibRef

Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.], Pekalska, E.[Elÿzbieta], Duin, R.P.W.[Robert P. W.],
Spherical and Hyperbolic Embeddings of Data,
PAMI(36), No. 11, November 2014, pp. 2255-2269.
IEEE DOI 1410
BibRef
Earlier:
Spherical embeddings for non-Euclidean dissimilarities,
CVPR10(1903-1910).
IEEE DOI 1006
Eigenvalues and eigenfunctions BibRef

Pekalska, E.[Elÿzbieta], Duin, R.P.W.[Robert P.W.], Paclík, P.[Pavel],
Prototype selection for dissimilarity-based classifiers,
PR(39), No. 2, February 2006, pp. 189-208.
Elsevier DOI Award, Pattern Recognition. 0512
BibRef

Harol, A.[Artsiom], Pekalska, E.[Elÿzbieta], Verzakov, S.[Sergey], Duin, R.P.W.[Robert P. W.],
Augmented Embedding of Dissimilarity Data into (Pseudo-)Euclidean Spaces,
SSPR06(613-621).
Springer DOI 0608
BibRef

Pekalska, E.[Elÿzbieta], Harol, A.[Artsiom], Duin, R.P.W.[Robert P.W.], Spillmann, B.[Barbara], Bunke, H.[Horst],
Non-Euclidean or Non-metric Measures Can Be Informative,
SSPR06(871-880).
Springer DOI 0608
BibRef

Duin, R.P.W.[Robert P. W.], Pekalska, E.[Elÿzbieta], Harol, A.[Artsiom], Lee, W.J.[Wan-Jui], Bunke, H.[Horst],
On Euclidean Corrections for Non-Euclidean Dissimilarities,
SSPR08(551-561).
Springer DOI 0812
BibRef

Pekalska, E.[Elÿzbieta], Duin, R.P.W.[Robert P.W.],
Dissimilarity-based classification for vectorial representations,
ICPR06(III: 137-140).
IEEE DOI 0609
BibRef

Fränti, P.[Pasi], Xu, M.[Mantao], Kärkkäinen, I.[Ismo],
Classification of binary vectors by using SC distance to minimize stochastic complexity,
PRL(24), No. 1-3, January 2003, pp. 65-73.
HTML Version. 0211
BibRef

Franti, P., Xu, M.[Mantao],
Classification of binary vectors by using Delta SC-distance,
ICPR02(II: 52-55).
IEEE DOI 0211
BibRef

Fred, A.L.N., Leitão, J.M.N.,
A new cluster isolation criterion based on dissimilarity increments,
PAMI(25), No. 8, August 2003, pp. 944-958.
IEEE Abstract. 0308
BibRef
Earlier:
Clustering Under a Hypothesis of Smooth Dissimilarity Increments,
ICPR00(Vol II: 190-194).
IEEE DOI 0009
BibRef

Aidos, H.[Helena], Fred, A.L.N.[Ana L.N.],
Statistical modeling of dissimilarity increments for d-dimensional data: Application in partitional clustering,
PR(45), No. 9, September 2012, pp. 3061-3071.
Elsevier DOI 1206
BibRef
Earlier:
On the Distribution of Dissimilarity Increments,
IbPRIA11(192-199).
Springer DOI 1106
Dissimilarity increments; Partitional clustering; Likelihood-ratio test; Minimum description length; Gaussian mixture decomposition BibRef

Aidos, H.[Helena], Fred, A.L.N.[Ana L.N.],
Dissimilarity Increments Distribution in the Evidence Accumulation Clustering Framework,
IbPRIA13(535-542).
Springer DOI 1307
BibRef

Aidos, H.[Helena], Fred, A.L.N.[Ana L.N.],
k-Nearest Neighbor Classification Using Dissimilarity Increments,
ICIAR12(I: 27-33).
Springer DOI 1206
BibRef

Fred, A.L.N., Jain, A.K.,
Combining multiple clusterings using evidence accumulation,
PAMI(27), No. 6, June 2005, pp. 835-850.
IEEE Abstract. 0506
BibRef
Earlier:
Data clustering using evidence accumulation,
ICPR02(IV: 276-280).
IEEE DOI 0211
BibRef

Fred, A.L.N.[Ana L.N.], Jain, A.K.[Anil K.],
Cluster validation using a probabilistic attributed graph,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Fred, A.L.N.[Ana L. N.], Jain, A.K.[Anil K.],
Learning Pairwise Similarity for Data Clustering,
ICPR06(I: 925-928).
IEEE DOI 0609
BibRef

Fred, A.L.N., Jain, A.K.,
Robust data clustering,
CVPR03(II: 128-133).
IEEE DOI 0307
BibRef

Hoti, F.[Fabian], Holmström, L.[Lasse],
A semiparametric density estimation approach to pattern classification,
PR(37), No. 3, March 2004, pp. 409-419.
Elsevier DOI 0401
The data are first transformed so that the pattern vector components with the most non-Gaussian structure are separated from the Gaussian components. Nonparametric density estimation is then used to capture the non-Gaussian structure of the data while parametric Gaussian conditional density estimation is applied to the rest of the components. BibRef

Holmstrom, L., Hoti, F.,
Application of semiparametric density estimation to classification,
ICPR04(III: 371-374).
IEEE DOI 0409
BibRef

de Souza, R.M.C.R.[Renata M. C. R.], de Carvalho, F.A.T.[Francisco A.T.],
Clustering of interval data based on city-block distances,
PRL(25), No. 3, February 2004, pp. 353-365.
Elsevier DOI 0401
BibRef

de Carvalho, F.A.T.[Francisco A.T.], de Souza, R.M.C.R.[Renata M.C.R.], Chavent, M.[Marie], Lechevallier, Y.[Yves],
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data,
PRL(27), No. 3, February 2006, pp. 167-179.
Elsevier DOI 0512
BibRef

de Carvalho, F.A.T.[Francisco A.T.], de Souza, R.M.C.R.[Renata M.C.R.],
Unsupervised pattern recognition models for mixed feature-type symbolic data,
PRL(31), No. 5, 1 April 2010, pp. 430-443.
Elsevier DOI 1002
Symbolic data analysis; Clustering analysis; Mixed feature-type symbolic data; Adaptive distances; Partition interpretation indices; Cluster interpretation indices BibRef

de Carvalho, F.A.T.[Francisco A.T.], Csernel, M.[Marc], Lechevallier, Y.[Yves],
Clustering constrained symbolic data,
PRL(30), No. 11, 1 August 2009, pp. 1037-1045.
Elsevier DOI 0909
Symbolic Data Analysis; Clustering algorithms; Normal symbolic form; Constraints; Dissimilarity functions BibRef

de Carvalho, F.A.T.[Francisco A.T.], Lechevallier, Y.[Yves],
Partitional clustering algorithms for symbolic interval data based on single adaptive distances,
PR(42), No. 7, July 2009, pp. 1223-1236.
Elsevier DOI 0903
Symbolic data analysis; Partitional clustering methods; Symbolic interval data; Adaptive distances; Partition interpretation indices; Cluster interpretation indices BibRef

de Carvalho, F.A.T.[Francisco A.T.], Lechevallier, Y.[Yves],
Dynamic Clustering of Interval-Valued Data Based on Adaptive Quadratic Distances,
SMC-A(39), No. 6, November 2009, pp. 1295-1306.
IEEE DOI 0911
BibRef

Queiroz, S.[Sergio], de Carvalho, F.A.T.[Francisco A.T.], Lechevallier, Y.[Yves],
Nonlinear multicriteria clustering based on multiple dissimilarity matrices,
PR(46), No. 12, 2013, pp. 3383-3394.
Elsevier DOI 1308
Clustering analysis BibRef

de Carvalho, F.A.T.[Francisco A.T.], Lechevallier, Y.[Yves], de Melo, F.M.[Filipe M.],
Partitioning hard clustering algorithms based on multiple dissimilarity matrices,
PR(45), No. 1, 2012, pp. 447-464.
Elsevier DOI 1410
Partitioning clustering algorithms BibRef

Ferreira, M.R.P.[Marcelo R.P.], de Carvalho, F.A.T.[Francisco A.T.],
Kernel-based hard clustering methods in the feature space with automatic variable weighting,
PR(47), No. 9, 2014, pp. 3082-3095.
Elsevier DOI 1406
Kernel clustering BibRef

Goldfarb, L.[Lev],
What is distance and why do we need the metric model for pattern learning?,
PR(25), No. 4, April 1992, pp. 431-438.
Elsevier DOI 0401
BibRef

Li, X., Hall, N.S., Humphreys, G.W.,
Discrete distance and similarity measures for pattern candidate selection,
PR(26), No. 6, June 1993, pp. 843-851.
Elsevier DOI 0401
BibRef

Singh, S.[Sameer],
Multiresolution estimates of classification complexity,
PAMI(25), No. 12, December 2003, pp. 1534-1539.
IEEE Abstract. 0401
Purity and Neighborhood separability. Compare to probabilistic distance measures. BibRef

Lee, K.K.[Ki K.], Yoon, W.C.[Wan C.],
Adaptive classification with ellipsoidal regions for multidimensional pattern classification problems,
PRL(26), No. 9, 1 July 2005, pp. 1232-1243.
Elsevier DOI 0506
BibRef

Lee, Y.J.[Yong-Jin], Choi, S.J.[Seung-Jin],
Maximum within-cluster association,
PRL(26), No. 10, 15 July 2005, pp. 1412-1422.
Elsevier DOI 0506
BibRef

Su, M.C.[Mu-Chun], Liu, Y.C.[Yi-Chun],
A new approach to clustering data with arbitrary shapes,
PR(38), No. 11, November 2005, pp. 1887-1901.
Elsevier DOI 0509
BibRef

Begelfor, E., Werman, M.[Michael],
How to Put Probabilities on Homographies,
PAMI(27), No. 10, October 2005, pp. 1666-1670.
IEEE DOI 0509
Estimation of normal distributions. BibRef

Chen, C.Y.[Chien-Yu], Hwang, S.C.[Shien-Ching], Oyang, Y.J.[Yen-Jen],
A statistics-based approach to control the quality of subclusters in incremental gravitational clustering,
PR(38), No. 12, December 2005, pp. 2256-2269.
Elsevier DOI 0510
BibRef

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Kernel matching pursuit for large datasets,
PR(38), No. 12, December 2005, pp. 2385-2390.
Elsevier DOI 0510
Greedy algorithm to approximate discrimination function. BibRef

Liu, B.Y.[Ben-Yong],
Adaptive training of a kernel-based nonlinear discriminator,
PR(38), No. 12, December 2005, pp. 2419-2425.
Elsevier DOI 0510
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Kim, M.H.[Min-Ho], Ramakrishna, R.S.,
New indices for cluster validity assessment,
PRL(26), No. 15, November 2005, pp. 2353-2363.
Elsevier DOI 0510
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PR(39), No. 3, March 2006, pp. 424-431.
Elsevier DOI 0601
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Jiao, L.C.[Li-Cheng], Li, Q.[Qing],
Kernel matching pursuit classifier ensemble,
PR(39), No. 4, April 2006, pp. 587-594.
Elsevier DOI 0604
Kernel Matching Pursuit Classifier; Ensemble Method; KMPC ensemble; Pattern recognition BibRef

Bouchard, G.[Guillaume], and Celeux, G.[Gilles],
Selection of Generative Models in Classification,
PAMI(28), No. 4, April 2006, pp. 544-554.
IEEE DOI 0604
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Samko, O.[Oksana], Rosin, P.L.[Paul L.], Marshall, A.D.[A. Dave],
Selection of the optimal parameter value for the Isomap algorithm,
PRL(27), No. 9, July 2006, pp. 968-979.
Elsevier DOI Nonlinear dimensionality reduction; Manifold learning 0605
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Ozertem, U.[Umut], Erdogmus, D.[Deniz], Jenssen, R.[Robert],
Spectral feature projections that maximize Shannon mutual information with class labels,
PR(39), No. 7, July 2006, pp. 1241-1252.
Elsevier DOI 0606
Feature extraction; Mutual information; Optimal subspace projection BibRef

Hild, II, K.E.[Kenneth E.], Erdogmus, D.[Deniz], Torkkola, K.[Kari], Principe, J.C.[Jose C.],
Feature Extraction Using Information-Theoretic Learning,
PAMI(28), No. 9, September 2006, pp. 1385-1392.
IEEE DOI 0608
Train feature extraction independently of the classification. Maximize mutual information between the labels and the output of the feature extractor. BibRef

Jenssen, R.[Robert], Erdogmus, D.[Deniz], Hild, II, K.E.[Kenneth E.], Principe, J.C.[Jose C.], Eltoft, T.[Torbjørn],
Information cut for clustering using a gradient descent approach,
PR(40), No. 3, March 2007, pp. 796-806.
Elsevier DOI 0611
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Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic, Non-parametric Clustering,
EMMCVPR05(34-45).
Springer DOI 0601
Graph theoretic cut; Information theory; Parzen window density estimation; Clustering; Gradient descent optimization; Annealing BibRef

Blansché, A., Gançarski, P., Korczak, J.J.,
MACLAW: A modular approach for clustering with local attribute weighting,
PRL(27), No. 11, August 2006, pp. 1299-1306.
Elsevier DOI 0606
Complex data; Modular clustering; Feature weighting; Cooperative coevolution; Clustering criterion BibRef

Gancarski, P., Blansche, A., Wania, A.,
Comparison between two coevolutionary feature weighting algorithms in clustering,
PR(41), No. 3, March 2008, pp. 983-994.
Elsevier DOI 0711
Complex data; Modular clustering; Feature weighting; Cooperative coevolution BibRef

Forestier, G.[Germain], Derivaux, S.[Sébastien], Wemmert, C.[Cédric], Gançarski, P.[Pierre],
An Evolutionary Approach for Ontology Driven Image Interpretation,
EvoIASP08(xx-yy).
Springer DOI 0804
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Petitjean, F.[Francois], Ketterlin, A.[Alain], Gancarski, P.[Pierre],
A global averaging method for dynamic time warping, with applications to clustering,
PR(44), No. 3, March 2011, pp. 678-693.
Elsevier DOI 1011
Sequence analysis; Time series clustering; Dynamic time warping; Distance-based clustering; Time series averaging; DTW barycenter averaging; Global averaging; Satellite image time series BibRef

Kennedy, J., Mendes, R.,
Neighborhood Topologies in Fully Informed and Best-of-Neighborhood Particle Swarms,
SMC-C(36), No. 4, July 2006, pp. 515-519.
IEEE DOI 0606
Discover optimal regions by emulating neighbors. BibRef

Ng, M.K.[Michael K.], Li, M.J.J.[Mark Jun-Jie], Huang, J.Z.X.[Joshua Zhe-Xue], He, Z.Y.[Zeng-You],
On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm,
PAMI(29), No. 3, March 2007, pp. 503-507.
IEEE DOI 0702

See also Alternative Extension of the k-Means Algorithm for Clustering Categorical Data, An. BibRef

Haralick, R.M.[Robert M.], Harpaz, R.[Rave],
Linear manifold clustering in high dimensional spaces by stochastic search,
PR(40), No. 10, October 2007, pp. 2672-2684.
Elsevier DOI 0707
Clustering; Linear manifold; Subspace; Histogram thresholding; Data exploration; Random projections. Cluster center is not a single point, for dispersed centers. BibRef

Iwata, K.[Kazunori], Hayashi, A.[Akira],
A Redundancy-Based Measure of Dissimilarity among Probability Distributions for Hierarchical Clustering Criteria,
PAMI(30), No. 1, January 2008, pp. 76-88.
IEEE DOI 0711
Measure difference between clusters. BibRef

Jung, G.J., Oh, Y.H.,
Information Distance-Based Subvector Clustering for ASR Parameter Quantization,
SPLetters(15), No. 1, 2008, pp. 209-212.
IEEE DOI 0802
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Gao, H., Meng, X., Chen, T.,
New Design of Robust H-inf Filters for 2-D Systems,
SPLetters(15), No. 1, 2008, pp. 217-220.
IEEE DOI 0802
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Halkidi, M.[Maria], Vazirgiannis, M.[Michalis],
A density-based cluster validity approach using multi-representatives,
PRL(29), No. 6, 15 April 2008, pp. 773-786.
Elsevier DOI 0803
Cluster validity; Clustering; Quality assessment; Unsupervised learning BibRef

Toh, K.A.[Kar-Ann], Eng, H.L.[How-Lung],
Between Classification-Error Approximation and Weighted Least-Squares Learning,
PAMI(30), No. 4, April 2008, pp. 658-669.
IEEE DOI 0803
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Nguyen, C.H.[Canh Hao], Ho, T.B.[Tu Bao],
An efficient kernel matrix evaluation measure,
PR(41), No. 11, November 2008, pp. 3366-3372.
Elsevier DOI 0808
Classification; Kernel methods; Kernel matrix quality measure; Kernel target alignment; Class separability measure BibRef

Irpino, A.[Antonio], Verde, R.[Rosanna],
Dynamic clustering of interval data using a Wasserstein-based distance,
PRL(29), No. 11, 1 August 2008, pp. 1648-1658.
Elsevier DOI 0804
Interval data; Clustering; Wasserstein distance; Inertia BibRef

Boutsinas, B., Papastergiou, T.,
On clustering tree structured data with categorical nature,
PR(41), No. 12, December 2008, pp. 3613-3623.
Elsevier DOI 0810
Clustering; (Dis)similarity measures; Data mining BibRef

Xue, H.[Hui], Chen, S.C.[Song-Can], Yang, Q.A.[Qi-Ang],
Discriminatively regularized least-squares classification,
PR(42), No. 1, January 2009, pp. 93-104.
Elsevier DOI 0809
Classifier design; Discriminative information; Manifold learning; Pattern recognition BibRef

Zhong, C.M.[Cai-Ming], Miao, D.Q.[Duo-Qian], Wang, R.Z.[Rui-Zhi], Zhou, X.M.[Xin-Min],
DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points,
PRL(29), No. 16, 1 December 2008, pp. 2067-2077.
Elsevier DOI 0811
Divisive clustering; Automatic clustering; Furthest reference point; Dissimilarity measure; Peak; Spurious cluster BibRef

Zhong, C.M.[Cai-Ming], Miao, D.Q.[Duo-Qian], Wang, R.Z.[Rui-Zhi],
A graph-theoretical clustering method based on two rounds of minimum spanning trees,
PR(43), No. 3, March 2010, pp. 752-766.
Elsevier DOI 1001
Graph-based clustering; Well-separated cluster; Touching cluster; Two rounds of MST BibRef

Lopez-Rubio, E.[Ezequiel], Ortiz-de-Lazcano-Lobato, J.M.[Juan Miguel],
Soft clustering for nonparametric probability density function estimation,
PRL(29), No. 16, 1 December 2008, pp. 2085-2091.
Elsevier DOI 0811
Probability density estimation; Nonparametric modeling; Soft clustering; Parzen window BibRef

Lopez-Rubio, E.,
A Histogram Transform for Probability Density Function Estimation,
PAMI(36), No. 4, April 2014, pp. 644-656.
IEEE DOI 1404
Estimation BibRef

Marteau, P.F.[Pierre-François],
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching,
PAMI(31), No. 2, February 2009, pp. 306-318.
IEEE DOI 0901
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Lazebnik, S.[Svetlana], Raginsky, M.[Maxim],
Supervised Learning of Quantizer Codebooks by Information Loss Minimization,
PAMI(31), No. 7, July 2009, pp. 1294-1309.
IEEE DOI 0905
BibRef

Hausner, A.[Alejo],
A new clustering algorithm for coordinate-free data,
PR(43), No. 4, April 2010, pp. 1306-1319.
Elsevier DOI 1002
Cluster analysis; Graph coloring; Metric space; Partition BibRef

Feng, L.[Liang], Qiu, M.H.[Ming-Hui], Wang, Y.X.[Yu-Xuan], Xiang, Q.L.[Qiao-Liang], Yang, Y.F.[Yin-Fei], Liu, K.[Kai],
A fast divisive clustering algorithm using an improved discrete particle swarm optimizer,
PRL(31), No. 11, 1 August 2010, pp. 1216-1225.
Elsevier DOI 1008
Hierarchical clustering; Divisive clustering; Particle swarm optimizer BibRef

Davy, M.[Manuel], Tourneret, J.Y.[Jean-Yves],
Generative Supervised Classification Using Dirichlet Process Priors,
PAMI(32), No. 10, October 2010, pp. 1781-1794.
IEEE DOI 1008
Applied to the classification of altimetric waveforms backscattered from different surfaces. BibRef

Yang, Y., Xu, D., Nie, F., Yan, S., Zhuang, Y.,
Image Clustering Using Local Discriminant Models and Global Integration,
IP(19), No. 10, October 2010, pp. 2761-2773.
IEEE DOI 1003
clustering using local discriminant models and global integration. BibRef

Gao, X.F.[Xiao-Fang], Liang, J.[Jiye],
The dynamical neighborhood selection based on the sampling density and manifold curvature for isometric data embedding,
PRL(32), No. 2, 15 January 2011, pp. 202-209.
Elsevier DOI 1101
Manifold learning; Tangent space; Dynamical neighborhood; Sampling density; Manifold curvature BibRef

Lu, J.W.[Ji-Wen], Tan, Y.P.[Yap-Peng],
Nearest Feature Space Analysis for Classification,
SPLetters(18), No. 1, January 2011, pp. 55-58.
IEEE DOI 1101
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Yamada, M.[Makoto], Sugiyama, M.[Masashi], Wichern, G.[Gordon], Simm, J.[Jaak],
Improving the Accuracy of Least-Squares Probabilistic Classifiers,
IEICE(E94-D), No. 6, June 2011, pp. 1337-1340.
WWW Link. 1101
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Yamada, M., Sigal, L., Raptis, M., Toyoda, M., Chang, Y., Sugiyama, M.,
Cross-Domain Matching with Squared-Loss Mutual Information,
PAMI(37), No. 9, September 2015, pp. 1764-1776.
IEEE DOI 1508
Analytical models BibRef

Patra, B.K.[Bidyut K.], Nandi, S.[Sukumar], Viswanath, P.,
A distance based clustering method for arbitrary shaped clusters in large datasets,
PR(44), No. 12, December 2011, pp. 2862-2870.
Elsevier DOI 1107
Distance based clustering; Arbitrary shaped clusters; Leaders; Single-link; Hybrid clustering method; Large datasets BibRef

Lu, Y.G.[Yong-Gang], Wan, Y.[Yi],
Clustering by Sorting Potential Values (CSPV): A novel potential-based clustering method,
PR(45), No. 9, September 2012, pp. 3512-3522.
Elsevier DOI 1206
Clustering; Potential field; Spatial distribution; Distance matrix; Pattern recognition BibRef

Lu, Y.G.[Yong-Gang], Wan, Y.[Yi],
PHA: A fast potential-based hierarchical agglomerative clustering method,
PR(46), No. 5, May 2013, pp. 1227-1239.
Elsevier DOI 1302
Clustering; Algorithm; Pattern recognition; Potential field BibRef

Wei, X.[Xin], Yang, Z.[Zhen],
The infinite Student's t-factor mixture analyzer for robust clustering and classification,
PR(45), No. 12, December 2012, pp. 4346-4357.
Elsevier DOI 1208
Infinite Student's t-factor mixture analyzer; Nonparametric Bayesian statistics; Variational inference; Clustering; Classification BibRef

Hatamlou, A.[Abdolreza],
In search of optimal centroids on data clustering using a binary search algorithm,
PRL(33), No. 13, 1 October 2012, pp. 1756-1760.
Elsevier DOI 1208
A binary search algorithm; Optimal centroids; Data clustering BibRef

Pei, T.[Tao], Gao, J.H.[Jian-Huan], Ma, T.[Ting], Zhou, C.H.[Cheng-Hu],
Multi-scale decomposition of point process data,
GeoInfo(16), No. 4, October 2012, pp. 625-652.
WWW Link. 1210
arbitrarily shaped clusters in point data. BibRef

Wang, Z.L.[Zi-Lei], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng], Xi, H.S.[Hong-Sheng],
Linear Distance Coding for Image Classification,
IP(22), No. 2, February 2013, pp. 537-548.
IEEE DOI 1302
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Wang, Z.L.[Zi-Lei], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng], Xi, H.S.[Hong-Sheng],
Image Classification via Object-Aware Holistic Superpixel Selection,
IP(22), No. 11, 2013, pp. 4341-4352.
IEEE DOI 1310
clutter BibRef

Wang, Z.L.[Zi-Lei], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng],
Collaborative Linear Coding for Robust Image Classification,
IJCV(114), No. 2-3, September 2015, pp. 322-333.
Springer DOI 1509
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Mohammadi, A., Asif, A.,
Decentralized Conditional Posterior Cramér-Rao Lower Bound for Nonlinear Distributed Estimation,
SPLetters(20), No. 2, February 2013, pp. 165-168.
IEEE DOI 1302
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Mu, Y.[Yang], Ding, W.[Wei], Tao, D.C.[Da-Cheng],
Local discriminative distance metrics ensemble learning,
PR(46), No. 8, August 2013, pp. 2337-2349.
Elsevier DOI 1304
Local learning; Distance metrics learning BibRef

Mai, H.T.[Hai Thanh], Kim, J.[Jaeho], Roh, Y.J.[Yohan J.], Kim, M.H.[Myoung Ho],
STHist-C: A highly accurate cluster-based histogram for two and three dimensional geographic data points,
GeoInfo(17), No. 2, April 2013, pp. 325-352.
Springer DOI 1304
optimizing queries. Find cluster centers, expand them out. BibRef

Bai, L.[Liang], Liang, J.[Jiye], Dang, C.Y.[Chuang-Yin], Cao, F.Y.[Fu-Yuan],
The Impact of Cluster Representatives on the Convergence of the K-Modes Type Clustering,
PAMI(35), No. 6, June 2013, pp. 1509-1522.
IEEE DOI 1305
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Bishnu, P.S.[Partha Sarathi], Prasad, S.[Saurabh], Bhattacherjee, V.[Vandana],
Volume-based clustering for arbitrary shaped clusters,
IJCVR(3), No. 3, 2013, pp. 167-181.
DOI Link 1309
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Araújo, D.[Daniel], Neto, A.D.[Adrião Dória], Martins, A.[Allan],
Representative cross information potential clustering,
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Elsevier DOI 1310
Clustering. Interactions between distributions. BibRef

Moreno, R.[Rodrigo], Koppal, S.[Sandeep], de Muinck, E.[Ebo],
Robust estimation of distance between sets of points,
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Elsevier DOI 1310
Spatial statistics BibRef

Yu, Y.W.[Ying-Wei], Gutierrez-Osuna, R.[Ricardo], Choe, Y.[Yoonsuck],
Context-sensitive intra-class clustering,
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Elsevier DOI 1402
Clustering BibRef

Anand, S., Mittal, S., Tuzel, O.[Oncel], Meer, P.[Peter],
Semi-Supervised Kernel Mean Shift Clustering,
PAMI(36), No. 6, June 2014, pp. 1201-1215.
IEEE DOI 1406
Clustering algorithms BibRef

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Kernel methods for weakly supervised mean shift clustering,
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IEEE DOI 0909
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Vu, V.V.[Viet-Vu], Labroche, N.[Nicolas], Bouchon-Meunier, B.[Bernadette],
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PR(45), No. 4, 2012, pp. 1749-1758.
Elsevier DOI 1410
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An Efficient Active Constraint Selection Algorithm for Clustering,
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IEEE DOI 1008
Active semi-supervised clustering BibRef

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Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
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Springer DOI 1210
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Kobayashi, T.[Takumi], Yoshikawa, F.[Fumito], Otsu, N.[Nobuyuki],
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IEEE DOI 1009
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Earlier: A1, A3, Only:
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IEEE DOI 0812
non-negative feature values BibRef

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IEEE DOI 1008
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Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
Bag of Hierarchical Co-occurrence Features for Image Classification,
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IEEE DOI 1008
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Earlier:
Efficient reduction of support vectors in kernel-based methods,
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IEEE DOI 0911
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Springer DOI 1612
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Earlier:
Structured Feature Similarity with Explicit Feature Map,
CVPR16(1211-1219)
IEEE DOI 1612
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Earlier:
Discriminative Local Binary Pattern for Image Feature Extraction,
CAIP15(I:594-605).
Springer DOI 1511
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Kobayashi, T.[Takumi],
S3CCA: Smoothly Structured Sparse CCA for Partial Pattern Matching,
ICPR14(1981-1986)
IEEE DOI 1412
Arrays BibRef

Kobayashi, T.[Takumi],
Dirichlet-Based Histogram Feature Transform for Image Classification,
CVPR14(3278-3285)
IEEE DOI 1409
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Kobayashi, T.[Takumi],
Learning Additive Kernel For Feature Transformation and Its Application to CNN Features,
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Local sets BibRef

Cleuziou, G.[Guillaume], Moreno, J.G.[Jose G.],
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Elsevier DOI 1506
Pattern recognition. Clusters with point symmetric shape. BibRef

Du, W.S.[Wen Sheng], Hu, B.Q.[Bao Qing],
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Intuitionistic fuzzy set BibRef

Bhargavi, M.S., Gowda, S.D.[Sahana D.],
A novel validity index with dynamic cut-off for determining true clusters,
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Clustering BibRef

Hao, H.[Hua], Wang, Q.L.[Qi-Long], Li, P.H.[Pei-Hua], Zhang, L.[Lei],
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Elsevier DOI 1605
Earth Mover's Distance. Gaussian mixture models. Texture classification BibRef

Duong, T.[Tarn], Beck, G.[Gaël], Azzag, H.[Hanene], Lebbah, M.[Mustapha],
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Elsevier DOI 1609
Gradient ascent BibRef

Lu, N.[Na], Miao, H.Y.[Hong-Yu],
Clustering Tree-Structured Data on Manifold,
PAMI(38), No. 10, October 2016, pp. 1956-1968.
IEEE DOI 1609
Algorithm design and analysis BibRef

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Effectively clustering by finding density backbone based-on kNN,
PR(60), No. 1, 2016, pp. 486-498.
Elsevier DOI 1609
Clustering algorithm BibRef

Tan, P.[Pan], Zhou, Z.C.[Zheng-Chun], Zhang, D.[Dan],
A Construction of Codebooks Nearly Achieving the Levenstein Bound,
SPLetters(23), No. 10, October 2016, pp. 1306-1309.
IEEE DOI 1610
product codes BibRef

Kuncheva, L.I.[Ludmila I.], Rodríguez, J.J.[Juan J.], Jackson, A.S.[Aaron S.],
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Elsevier DOI 1612
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Earlier: A1, A3, Only:
Who Is Missing? A New Pattern Recognition Puzzle,
SSSPR14(243-252).
Springer DOI 1408
At most 1 object from each class, assign all objects, find missing classes. Hungarian assignment algorithm. Pattern recognition. What is the object, which ones are not there, which ones are there. E.g. tracking fish in a tank. BibRef

Kerimbekov, Y.[Yerzhan], Bilge, H.S.[Hasan Sakir], Ugurlu, H.H.[Hasan Hüseyin],
The use of Lorentzian distance metric in classification problems,
PRL(84), No. 1, 2016, pp. 170-176.
Elsevier DOI 1612
Lorentzian distance metric BibRef

Zong, L.L.[Lin-Lin], Zhang, X.C.[Xian-Chao], Yu, H.[Hong], Zhao, Q.L.[Qian-Li], Ding, F.[Feng],
Local linear neighbor reconstruction for multi-view data,
PRL(84), No. 1, 2016, pp. 56-62.
Elsevier DOI 1612
Multi-view similarity BibRef

Lipsa, G.M.[Gabriel M.], Guerriero, M.[Marco],
A Geometrical Look at MOSPA Estimation Using Transportation Theory,
SPLetters(23), No. 12, December 2016, pp. 1835-1838.
IEEE DOI 1612
computational geometry BibRef

Lin, K.F.[Keng-Fan], Perissin, D.[Daniele],
Identification of Statistically Homogeneous Pixels Based on One-Sample Test,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link 1702
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Yang, M.[Meng], Wang, X.[Xing], Liu, W.Y.[Wei-Yang], Shen, L.L.[Lin-Lin],
Joint regularized nearest points for image set based face recognition,
IVC(58), No. 1, 2017, pp. 47-60.
Elsevier DOI 1703
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Earlier: A1, A3, A4, Only: FG15(1-7)
IEEE DOI 1508
face recognition BibRef

Yang, M.[Meng], Zhu, P.F.[Peng-Fei], Van Gool, L.J., Zhang, L.[Lei],
Face Recognition Based on Regularized Nearest Points Between Image Sets,
FG13(1-7)
IEEE DOI 1309
face recognition. Cluster distances. BibRef

Wang, Y., Zhang, L., Deng, H., Lu, J., Huang, H., Zhang, L., Liu, J., Tang, H., Xing, X.,
Learning a Discriminative Distance Metric With Label Consistency for Scene Classification,
GeoRS(55), No. 8, August 2017, pp. 4427-4440.
IEEE DOI 1708
Encoding, Feature extraction, Learning systems, Measurement, Optimization, Remote sensing, Spatial resolution, Distance metric learning (DML), high spatial resolution (HSR), label consistency (LC), optimization, scene, classification BibRef

Datta, S.[Shounak], Mullick, S.S.[Sankha Subhra], Das, S.[Swagatam],
Generalized mean based back-propagation of errors for ambiguity resolution,
PRL(94), No. 1, 2017, pp. 22-29.
Elsevier DOI 1708
Ambiguity resolution. Datapoints have multiple labels. BibRef

Ortakaya, A.F.[Ahmet Fatih],
Independently weighted value difference metric,
PRL(97), No. 1, 2017, pp. 61-68.
Elsevier DOI 1709
Categorical classification BibRef

Mao, Q.[Qi], Wang, L.[Li], Tsang, I.W.[Ivor W.], Sun, Y.J.[Yi-Jun],
Principal Graph and Structure Learning Based on Reversed Graph Embedding,
PAMI(39), No. 11, November 2017, pp. 2227-2241.
IEEE DOI 1710
Bifurcation, Cancer, Convergence, Grammar, Manifolds, Optical imaging, Skeleton, Principal curve, principal graph, structure, learning BibRef

Wang, L.[Li], Mao, Q.[Qi],
Probabilistic Dimensionality Reduction via Structure Learning,
PAMI(41), No. 1, January 2019, pp. 205-219.
IEEE DOI 1812
Data models, Probabilistic logic, Manifolds, Kernel, Principal component analysis, Data visualization, latent variable model BibRef

Tian, J.Y.[Jin-Yu], Zhang, T.P.[Tai-Ping], Qin, A.Y.[An-Yong], Shang, Z.W.[Zhao-Wei], Tang, Y.Y.[Yuan Yan],
Learning the Distribution Preserving Semantic Subspace for Clustering,
IP(26), No. 12, December 2017, pp. 5950-5965.
IEEE DOI 1710
revised kernel density estimator, Clustering algorithms, Euclidean distance, Indexing, Kernel, Manifolds, BibRef

Chakraborty, S.[Saptarshi], Das, S.[Swagatam],
Means clustering with a new divergence-based distance metric: Convergence and performance analysis,
PRL(100), No. 1, 2017, pp. 67-73.
Elsevier DOI 1712
k-means clustering BibRef

Yang, X.H.[Xu-Hua], Zhu, Q.P.[Qin-Peng], Huang, Y.J.[Yu-Jiao], Xiao, J.[Jie], Wang, L.[Lei], Tong, F.C.[Fei-Chang],
Parameter-free Laplacian centrality peaks clustering,
PRL(100), No. 1, 2017, pp. 167-173.
Elsevier DOI 1712
Weighted complete graph BibRef

Gao, R.Q.[Ri-Qiang], Yang, F.[Fuwei], Yang, W.M.[Wen-Ming], Liao, Q.M.[Qing-Min],
Margin Loss: Making Faces More Separable,
SPLetters(25), No. 2, February 2018, pp. 308-312.
IEEE DOI 1802
Margin loss aims to focus on samples hard to classify by a distance margin. face recognition, image representation, learning (artificial intelligence), Euclidean distances, margin loss BibRef

Miao, Q.[Qing], Ling, B.W.K.[Bingo Wing-Kuen],
Analytical form of globally optimal solution of weighted sum of intraclass separation and interclass separation,
SIViP(12), No. 3, March 2018, pp. 393-401.
WWW Link. 1804
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Li, Y.Y.[Yang-Yang],
Curvature-aware manifold learning,
PR(83), 2018, pp. 273-286.
Elsevier DOI 1808
Manifold learning, Riemannian curvature, Second fundamental form, Hessian operator BibRef

Lee, S.H.[Soo-Hyun], Jeong, Y.S.[Young-Seon], Kim, J.Y.[Jae-Yun], Jeong, M.K.[Myong K.],
A new clustering validity index for arbitrary shape of clusters,
PRL(112), 2018, pp. 263-269.
Elsevier DOI 1809
Arbitrary shape of clusters, Clustering, Cluster validity index, Kernel space, SVDD BibRef

Wang, M.J.[Meng-Jiao], Panagakis, Y.[Yannis], Snape, P.[Patrick], Zafeiriou, S.P.[Stefanos P.],
Disentangling the Modes of Variation in Unlabelled Data,
PAMI(40), No. 11, November 2018, pp. 2682-2695.
IEEE DOI 1810
Tensile stress, Lighting, Shape, Matrix decomposition, Visualization, Principal component analysis, expression transfer BibRef

Wang, Q., Lu, X., Li, P., Gao, Z., Piao, Y.,
An Information Geometry-Based Distance Between High-Dimensional Covariances for Scalable Classification,
CirSysVideo(28), No. 10, October 2018, pp. 2449-2459.
IEEE DOI 1811
Measurement, Covariance matrices, Manifolds, Visualization, Kernel, Support vector machines, Gaussian distribution, Fisher metric BibRef

Xiao, Y.Y.[Yi-Yong], Huang, C.H.[Chang-Hao], Huang, J.Y.[Jiao-Ying], Kaku, I.[Ikou], Xu, Y.C.[Yu-Chun],
Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering,
PR(90), 2019, pp. 183-195.
Elsevier DOI 1903
Categorical clustering, Variable neighborhood search, Data mining, Integer linear programming BibRef

Rastin, P.[Parisa], Cabanes, G.[Guénaël], Matei, B.[Basarab], Bennani, Y.[Younès], Marty, J.M.[Jean-Marc],
A new sparse representation learning of complex data: Application to dynamic clustering of web navigation,
PR(91), 2019, pp. 291-307.
Elsevier DOI 1904
Clustering, Relational data, Barycentric coordinates, Data stream BibRef

Nitzan, E., Routtenberg, T., Tabrikian, J.,
Cramér-Rao Bound Under Norm Constraint,
SPLetters(26), No. 9, September 2019, pp. 1393-1397.
IEEE DOI 1909
Cramer-Rao bounds, Parameter estimation, Matrices, Benchmark testing, Maximum likelihood estimation, Lehmann-unbiasedness BibRef

Chen, Y.[Yi], Billard, L.,
A study of divisive clustering with Hausdorff distances for interval data,
PR(96), 2019, pp. 106969.
Elsevier DOI 1909
Interval data, Divisive clustering, Hausdorff distances, Gowda-Diday distances, Ichino-Yaguchi distances, Local and global normalizations BibRef

Ye, Y.F.[Yun-Fei], Han, D.[Dong],
Multi-distance support matrix machines,
PRL(128), 2019, pp. 237-243.
Elsevier DOI 1912
Multi-distance support matrix machine, Generalization bounds, Rademacher complexity, Vapnik-Chervonenkis dimension BibRef

Flores, J.L.[Jose Luis], Calvo, B.[Borja], Perez, A.[Aritz],
Supervised non-parametric discretization based on Kernel density estimation,
PRL(128), 2019, pp. 496-504.
Elsevier DOI 1912
discretizing continuous attributes before classification. Discretization, Supervised, Non-parametric, Kernel density BibRef

Dong, M.Z.[Ming-Zhi], Wang, Y.J.[Yu-Jiang], Yang, X.C.[Xiao-Chen], Xue, J.H.[Jing-Hao],
Learning Local Metrics and Influential Regions for Classification,
PAMI(42), No. 6, June 2020, pp. 1522-1529.
IEEE DOI 2005
Measurement, Task analysis, Learning systems, Mathematical model, Fasteners, Artificial neural networks, Clustering algorithms, local metric BibRef

Iglesias, F.[Félix], Zseby, T.[Tanja], Zimek, A.[Arthur],
Absolute Cluster Validity,
PAMI(42), No. 9, September 2020, pp. 2096-2112.
IEEE DOI 2008
Clustering algorithms, Indexes, Benchmark testing, Task analysis, Proposals, Autonomous systems, Clustering, cluster validity BibRef

Sarkar, S.[Soham], Ghosh, A.K.[Anil K.],
On Perfect Clustering of High Dimension, Low Sample Size Data,
PAMI(42), No. 9, September 2020, pp. 2257-2272.
IEEE DOI 2008
Clustering algorithms, Indexes, Euclidean distance, Sociology, Statistics, Single photon emission computed tomography, Rand index BibRef

Qiu, T.[Teng], Li, Y.J.[Yong-Jie],
Enhancing in-tree-based clustering via distance ensemble and kernelization,
PR(112), 2021, pp. 107731.
Elsevier DOI 2102
In-tree, Distance ensemble, Kernelization, Clustering BibRef

Xie, J.Y.[Juan-Ying], Wang, M.Z.[Ming-Zhao], Lu, X.X.[Xiao-Xiao], Liu, X.L.[Xing-Lin], Grant, P.W.[Philip W.],
DP-k-modes: A self-tuning k-modes clustering algorithm,
PRL(158), 2022, pp. 117-124.
Elsevier DOI 2205
Modes clustering, Local density, Density peaks, Standard deviation, Initial seeds BibRef

Hadi, A.S.[Ali S.],
A new distance between multivariate clusters of varying locations, elliptical shapes, and directions,
PR(129), 2022, pp. 108780.
Elsevier DOI 2206
Clustering methods, Complete linkage, Elliptical distance, Euclidean distance, Hamming distance, Hierarchical clustering, Ward method BibRef

Yan, H.[He], Fu, L.[Liyong], Zhang, T.[Tian'an], Hu, J.[Jun], Ye, Q.L.[Qiao-Lin], Qi, Y.[Yong], Yu, D.J.[Dong-Jun],
Robust distance metric optimization driven GEPSVM classifier for pattern classification,
PR(129), 2022, pp. 108779.
Elsevier DOI 2206
Classification problem, Distance metric learning, Outliers and noises, Robust L-GEPSVM method, Squared L-norm distance BibRef

Zhang, Y.Q.[Yi-Qun], Cheung, Y.M.[Yiu-Ming],
Learnable Weighting of Intra-Attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes,
PAMI(44), No. 7, July 2022, pp. 3560-3576.
IEEE DOI 2206
Clustering algorithms, Weight measurement, Measurement, Loss measurement, Encoding, Task analysis, Partitioning algorithms, learnable weighting BibRef

Rasool, Z.[Zafaryab], Aryal, S.I.[Sun-Il], Bouadjenek, M.R.[Mohamed Reda], Dazeley, R.[Richard],
Overcoming weaknesses of density peak clustering using a data-dependent similarity measure,
PR(137), 2023, pp. 109287.
Elsevier DOI 2302
Clustering, Density peak clustering, Similarity measure, Data-dependent similarity BibRef

Cao, X.F.[Xiao-Feng],
Poincaré Fréchet mean,
PR(137), 2023, pp. 109302.
Elsevier DOI 2302
Fréchet mean, Hyperbolic geometry, Poincaré model, Minimizing upper bound, ()-approximation BibRef

Mousavi, E.[Elahe], Sehhati, M.[Mohammadreza],
A generalized multi-aspect distance metric for mixed-type data clustering,
PR(138), 2023, pp. 109353.
Elsevier DOI 2303
Clustering, Mixed data, Ordinal and nominal attribute, Inter-dependency, Intra-attribute information, Mutual information BibRef

Bayer, T.[Tomáš], Kolingerová, I.[Ivana], Potucková, M.[Markéta], Cábelka, M.[Miroslav], Štefanová, E.[Eva],
An incremental facility location clustering with a new hybrid constrained pseudometric,
PR(141), 2023, pp. 109520.
Elsevier DOI 2306
Facility location, Clusterization, Pseudometric, Detection, Simplification, Point cloud BibRef

Capó, M.[Marco], Pérez, A.[Aritz], Lozano, J.A.[Jose A.],
Fast computation of cluster validity measures for Bregman divergences and benefits,
PRL(170), 2023, pp. 100-105.
Elsevier DOI 2306
Partitional clustering, Number of clusters, Silhouette index, Davies-Bouldin, Calinski-Harabasz, Bregman divergences BibRef

Wang, W.[Wei], Wu, Z.W.[Zi-Wen], Zhang, M.W.[Ming-Wei], Li, Y.[Yue],
SDWD: Style Diversity Weighted Distance Evaluates the Intra-Class Data Diversity of Distributed GANs,
ICIP23(1925-1929)
IEEE DOI 2312
BibRef

Kar, A.K.[Amit Kumar], Akhter, M.M.[Mohammad Maksood], Mishra, A.C.[Amaresh Chandra], Mohanty, S.K.[Sraban Kumar],
EDMD: An Entropy based Dissimilarity measure to cluster Mixed-categorical Data,
PR(155), 2024, pp. 110674.
Elsevier DOI 2408
Proximity measure, Mixed categorical data, Ordinal attributes, Nominal attributes, Entropy, Dissimilarity measure BibRef

Battistella, E.[Enzo], Vakalopoulou, M.[Maria], Paragios, N.[Nikos], Deutsch, É.[Éric],
GHOST: Graph-based higher-order similarity transformation for classification,
PR(155), 2024, pp. 110623.
Elsevier DOI 2408
Feature selection, Distance learning, Higher-order BibRef

Chen, M.[Mei], Zhang, J.H.[Jin-Hong], Zhang, C.[Chi], Ma, X.Y.[Xue-Yan], Qian, L.X.[Luo-Xiong],
Density change consistency clustering from density extreme,
PR(158), 2025, pp. 110932.
Elsevier DOI 2411
Density-based clustering, Density extreme, Heterogeneous density, Density change consistency, Cluster merging BibRef

Wilson, M.[Michael], Needham, T.[Tom], Park, C.[Chiwoo], Kundu, S.[Suparteek], Srivastava, A.[Anuj],
A Wasserstein-Type Distance for Gaussian Mixtures on Vector Bundles with Applications to Shape Analysis,
SIIMS(17), No. 3, 2024, pp. 1433-1466.
DOI Link 2501
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Li, R.[Ran], Chen, G.L.[Guang-Liang],
Fast, Memory-efficient Spectral Clustering with Cosine Similarity,
CIARP23(I:700-714).
Springer DOI 2312
BibRef

Levada, A.L.M.[Alexandre L. M.],
A Curvature based Isometric Feature Mapping,
ICPR22(557-563)
IEEE DOI 2212
Clustering algorithms, Pattern classification, Optimization methods, Euclidean distance, Feature extraction, Manifold learning BibRef

Kaul, C.[Chaitanya], Pears, N.[Nick], Dai, H.[Hang], Murray-Smith, R.[Roderick], Manandhar, S.[Suresh],
Penalizing Small Errors Using an Adaptive Logarithmic Loss,
AIHA20(368-375).
Springer DOI 2103
Or used in NN training. BibRef

Tsubota, K.[Koki], Aizawa, K.[Kiyoharu],
Unsupervised Embedding Learning by Noisy Similarity Label Optimization,
VCIP20(247-250)
IEEE DOI 2102
Noise measurement, Training, Integrated circuits, Testing, Semantics, Optimization, Entropy BibRef

Ishii, Y., Iwao, K., Kinoshita, T.,
Characteristics of the Degree of Grade in Grade-added Rough Set For Land Cover Classification,
Environmental19(19-24).
DOI Link 1904
Similar to SVM technique. BibRef

Law, M.T.[Marc T.], Weng, P.[Paul],
Representing Relative Visual Attributes with a Reference-Point-Based Decision Model,
ICPR18(435-440)
IEEE DOI 1812
Visualization, Euclidean distance, Decision theory, Computational modeling, Machine learning, Semantics BibRef

Lin, W.Y.[Wen-Yan], Lai, J.H.[Jian-Huang], Liu, S.Y.[Si-Ying], Matsushita, Y.[Yasuyuki],
Dimensionality's Blessing: Clustering Images by Underlying Distribution,
CVPR18(5784-5793)
IEEE DOI 1812
Random variables, Clustering algorithms, Sensors, Machine learning, Task analysis, Measurement BibRef

Yin, H., Li, F., Zhang, L.,
Multi-Source Clustering based on spectral recovery,
ICPR18(231-236)
IEEE DOI 1812
Clustering algorithms, Symmetric matrices, Laplace equations, Eigenvalues and eigenfunctions, Matrix decomposition, multi-view spectral embedding BibRef

Dornaika, F.[Fadi], El Traboulsi, Y.[Youssof], Zhu, R.,
Robust and Flexible Graph-based Semi-supervised Embedding,
ICPR18(465-470)
IEEE DOI 1812
Symmetric matrices, Laplace equations, Feature extraction, Semisupervised learning, Linear regression, robust loss function BibRef

Gao, S., Dai, J., Shi, H.,
Discernibility Matrix-Based Ensemble Learning,
ICPR18(952-957)
IEEE DOI 1812
Rough sets, Bagging, Symmetric matrices, Training, Clustering algorithms, Computer science, Machine learning BibRef

Luo, X., Zhang, L., Li, F., Wang, B.,
Graph Embedding-Based Ensemble Learning for Image Clustering,
ICPR18(213-218)
IEEE DOI 1812
Clustering algorithms, Symmetric matrices, Machine learning algorithms, Power capacitors, Manifolds, image clustering BibRef

Iscen, A.[Ahmet], Avrithis, Y.[Yannis], Tolias, G.[Giorgos], Furon, T.[Teddy], Chum, O.[Ondrej],
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking,
ACCV18(II:301-316).
Springer DOI 1906
BibRef

Iscen, A.[Ahmet], Tolias, G.[Giorgos], Avrithis, Y.[Yannis], Furon, T.[Teddy], Chum, O.[Ondrej],
Fast Spectral Ranking for Similarity Search,
CVPR18(7632-7641)
IEEE DOI 1812
Sparse matrices, Symmetric matrices, Eigenvalues and eigenfunctions, Approximation algorithms, Matrix decomposition BibRef

Koniusz, P., Zhang, H., Porikli, F.M.[Fatih M.],
A Deeper Look at Power Normalizations,
CVPR18(5774-5783)
IEEE DOI 1812
Kernel, Feature extraction, Visualization, Symmetric matrices, Eigenvalues and eigenfunctions, Task analysis BibRef

Gou, M., Xiong, F., Camps, O., Sznaier, M.,
MoNet: Moments Embedding Network,
CVPR18(3175-3183)
IEEE DOI 1812
Symmetric matrices, Matrices, Backpropagation, Encoding, Task analysis, Neural networks BibRef

Mi, L.[Liang], Zhang, W.[Wen], Gu, X.F.[Xian-Feng], Wang, Y.L.[Ya-Lin],
Variational Wasserstein Clustering,
ECCV18(XV: 336-352).
Springer DOI 1810
BibRef

Figueroa, K.[Karina], Reyes, N.[Nora], Camarena-Ibarrola, A.[Antonio], Valero-Elizondo, L.,
Improving the List of Clustered Permutation on Metric Spaces for Similarity Searching on Secondary Memory,
MCPR18(82-92).
Springer DOI 1807
BibRef

Kolouri, S.[Soheil], Zou, Y.[Yang], Rohde, G.K.[Gustavo K.],
Sliced Wasserstein Kernels for Probability Distributions,
CVPR16(5258-5267)
IEEE DOI 1612
BibRef

Minh, H.Q., Biagio, M.S., Bazzani, L., Murino, V.,
Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification,
CVPR16(5195-5203)
IEEE DOI 1612
BibRef

Poddar, S.[Sunrita], Jacob, M.[Mathews],
Convex clustering and recovery of partially observed data,
ICIP16(3498-3502)
IEEE DOI 1610
Algorithm design and analysis. Groups of similar objects within a collection of objects. BibRef

Nielsen, F.[Frank], Muzellec, B.[Boris], Nock, R.[Richard],
Classification with mixtures of curved Mahalanobis metrics,
ICIP16(241-245)
IEEE DOI 1610
Covariance matrices BibRef

An, S.J.[Sen-Jian], Hayat, M.[Munawar], Khan, S.H.[Salman H.], Bennamoun, M.[Mohammed], Boussaid, F.[Farid], Sohel, F.A.[Ferdous A.],
Contractive Rectifier Networks for Nonlinear Maximum Margin Classification,
ICCV15(2515-2523)
IEEE DOI 1602
Aerospace electronics BibRef

Biswas, A.[Arijit], Jacobs, D.[David],
An Efficient Algorithm for Learning Distances that Obey the Triangle Inequality,
BMVC15(xx-yy).
DOI Link 1601
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Rahimi, A.M.[Amir M.], Nataraj, L.[Lakshmanan], Manjunath, B.S.,
Features we trust!,
ICIP15(3476-3480)
IEEE DOI 1512
Conditional Random Fields (CRF) BibRef

Zhen, M.M.[Ming-Min], Wang, W.M.[Wen-Min], Wang, R.G.[Rong-Gang],
Improved cluster center adaption for image classification,
ICIP15(3092-3095)
IEEE DOI 1512
Feature coding BibRef

Ye, J.B.[Jian-Bo], Li, J.[Jia],
Scaling up discrete distribution clustering using ADMM,
ICIP14(5267-5271)
IEEE DOI 1502
Clustering algorithms. alternating direction method of multipliers. BibRef

Sandhan, T.[Tushar], Yun, K.[Kimin], Choi, J.Y.[Jin Young],
Proximity Clustering for Revealing a Semantically Dominant Class,
ISVC14(II: 63-73).
Springer DOI 1501
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Lyon, R.J., Brooke, J.M., Knowles, J.D., Stappers, B.W.,
Hellinger Distance Trees for Imbalanced Streams,
ICPR14(1969-1974)
IEEE DOI 1412
Decision trees; Earth; Labeling; Remote sensing; Satellites; Skin; Training BibRef

Gan, Q.A.[Qi-Ang], Shen, F.[Furao], Zhao, J.X.[Jin-Xi],
An Extended Isomap for Manifold Topology Learning with SOINN Landmarks,
ICPR14(1579-1584)
IEEE DOI 1412
Clustering algorithms BibRef

Zhen, X.T.[Xian-Tong], Shao, L.[Ling], Zheng, F.[Feng],
Discriminative Embedding via Image-to-Class Distances,
BMVC14(xx-yy).
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applied in naive Bayes nearest neighbor. BibRef

Moreno-García, C.F.[Carlos Francisco], Serratosa, F.[Francesc],
Weighted Mean Assignment of a Pair of Correspondences Using Optimisation Functions,
SSSPR14(301-311).
Springer DOI 1408
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Purkait, P.[Pulak], Chin, T.J.[Tat-Jun], Sadri, A.[Alireza], Suter, D.[David],
Clustering with Hypergraphs: The Case for Large Hyperedges,
PAMI(39), No. 9, September 2017, pp. 1697-1711.
IEEE DOI 1708
Clustering algorithms, Computational modeling, Image segmentation, Motion segmentation, Sampling methods, Tensile stress, Higher order grouping, hypergraph clustering, motion, segmentation BibRef

Purkait, P.[Pulak], Chin, T.J.[Tat-Jun], Ackermann, H.[Hanno], Suter, D.[David],
C Clustering with Hypergraphs: The Case for Large Hyperedges,
ECCV14(IV: 672-687).
Springer DOI 1408
Same title, different 3rd author. BibRef

Dong, M.Z.[Ming-Zhi], Yin, L.[Liang], Deng, W.H.[Wei-Hong], Wang, Q.A.[Qi-Ang], Yuan, C.X.[Cai-Xia], Guo, J.[Jun], Shang, L.[Li], Ma, L.W.[Li-Wei],
A Linear Max K-min classifier,
ICPR12(2967-2971).
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Gu, Z.H.[Zheng-Hong], Shao, M.[Ming], Li, L.Y.[Liang-Yue], Fu, Y.[Yun],
Discriminative metric: Schatten norm vs. vector norm,
ICPR12(1213-1216).
WWW Link. 1302
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Quéré, R.[Romain], Frélicot, C.[Carl],
A New Index Based on Sparsity Measures for Comparing Fuzzy Partitions,
SSSPR12(291-300).
Springer DOI 1211
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Wang, Z.X.[Zheng-Xiang], Gao, S.H.[Sheng-Hua], Chia, L.T.[Liang-Tien],
Learning Class-to-Image Distance via Large Margin and L1-Norm Regularization,
ECCV12(II: 230-244).
Springer DOI 1210
BibRef

Levy, N.[Noga], Wolf, L.B.[Lior B.],
Minimal Correlation Classification,
ECCV12(VI: 29-42).
Springer DOI 1210
BibRef

Zhang, W.[Wei], Wang, X.G.[Xiao-Gang], Zhao, D.L.[De-Li], Tang, X.[Xiaoou],
Graph Degree Linkage: Agglomerative Clustering on a Directed Graph,
ECCV12(I: 428-441).
Springer DOI 1210
BibRef

Tsai, C.L.[Chia-Liang], Chien, S.Y.[Shao-Yi],
New optimization scheme for L2-norm total variation semi-supervised image soft labeling,
ICIP11(3369-3372).
IEEE DOI 1201
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Streib, K.[Kevin], Davis, J.W.[James W.],
Using Ripley's K-function to improve graph-based clustering techniques,
CVPR11(2305-2312).
IEEE DOI 1106
Multi-Distance Spatial Cluster Analysis
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Du, W.W.[Wei-Wei], Urahama, K.[Kiichi],
Semi-Supervised Spectral Mapping for Enhancing Separation between Classes,
MVA09(187-).
PDF File. 0905
BibRef
Earlier:
Error-correcting semi-supervised learning with mode-filter on graphs,
Emergent09(2095-2100).
IEEE DOI 0910
Apply mode filter to deal with errors in training data. BibRef

Xiao, R.[Rui], Zhao, Q.J.[Qi-Jun], Zhang, D.[David], Shi, P.F.[Peng-Fei],
Data Classification on Multiple Manifolds,
ICPR10(3898-3901).
IEEE DOI 1008
data from different classes may reide on different manifolds of different dimensions. BibRef

Rota Bulo, S.[Samuel], Pelillo, M.[Marcello],
Probabilistic Clustering Using the Baum-Eagon Inequality,
ICPR10(1429-1432).
IEEE DOI 1008
BibRef

Takahashi, T.[Tetsuji], Kudo, M.[Mineichi],
Margin Preserved Approximate Convex Hulls for Classification,
ICPR10(4052-4055).
IEEE DOI 1008
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Zhang, X.J.[Xian-Jun], Yao, M.[Min], Zhu, R.[Rong],
A novel image classification method based on manifold learning and Gaussian mixture model,
IASP10(243-247).
IEEE DOI 1004
coarse-grained using ISOMap for manifold learning. BibRef

Yang, X.W.[Xing-Wei], Latecki, L.J.[Longin Jan], Gross, A.D.[Ari D.],
Distance Learning Based on Convex Clustering,
ISVC09(II: 747-756).
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Washizawa, Y.[Yoshikazu],
Pattern Classification on Local Metric Structure,
ICDAR09(471-475).
IEEE DOI 0907
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Kryszczuk, K.[Krzysztof], Drygajlo, A.[Andrzej],
Impact of feature correlations on separation between bivariate normal distributions,
ICPR08(1-4).
IEEE DOI 0812
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Havens, T.C.[Timothy C.], Bezdek, J.C.[James C.], Keller, J.M.[James M.], Popescu, M.[Mihail],
Dunn's cluster validity index as a contrast measure of VAT images,
ICPR08(1-4).
IEEE DOI 0812
VAT: Visual Assessment of cluster Tendency.
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Wolf, L.B.[Lior B.], Donner, Y.[Yoni],
Local Regularization for Multiclass Classification Facing Significant Intraclass Variations,
ECCV08(IV: 748-759).
Springer DOI 0810
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Zhang, K.B.[Ke-Bing], Orgun, M.A.[Mehmet A.], Zhang, K.[Kang],
Enhanced Visual Separation of Clusters by M-Mapping to Facilitate Cluster Analysis,
Visual07(285-297).
Springer DOI 0706
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Dehzangi, O.[Omid], Zolghadri, M.J.[Mansoor J.], Taheri, S.[Shahram], Dehzangi, A.[Abdollah],
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CAIP07(970-978).
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Zhang, T.[Tao], Boult, T.E.[Terrance E.], Johnson, R.C.,
Two thresholds are better than one,
VS07(1-8).
IEEE DOI 0706
Rather than optimal Bayesian single threshold, better results with 2, use spatial cohesion. BibRef

Yamashita, Y.[Yukihiko], Numakami, M.[Mariko], Inoue, N.[Naoya],
Maxwell Normal Distribution in a Manifold and Mahalanobis Metric,
SSPR06(604-612).
Springer DOI 0608
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Mekuz, N.[Nathan], Tsotsos, J.K.[John K.],
Parameterless Isomap with Adaptive Neighborhood Selection,
DAGM06(364-373).
Springer DOI 0610
Isomap usually dependent on initial parameters. BibRef

Sun, X.C.[Xi-Chen], Cheng, Q.S.[Qian-Sheng],
On Subspace Distance,
ICIAR06(II: 81-89).
Springer DOI 0610
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Demirkol, A.[Askin], Demir, Z.[Zafer], Emre, E.[Erol],
Alternative Approaches and Algorithms for Classification,
ICIAR06(II: 35-46).
Springer DOI 0610
BibRef
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Estimation of Target Density Functions by a New Algorithm,
ICIAR05(1200-1207).
Springer DOI 0509
Centers of masses, new cost function. BibRef

Chen, J.[Jie], Wang, R.P.[Rui-Ping], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin], Gao, W.[Wen],
Isomap Based on the Image Euclidean Distance,
ICPR06(II: 1110-1113).
IEEE DOI 0609
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Wang, J.G.[Ji-Gang], Neskovic, P.[Predrag], Cooper, L.N.[Leon N.],
A Minimum Sphere Covering Approach to Pattern Classification,
ICPR06(III: 433-436).
IEEE DOI 0609
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Long, T.[Teng], Jin, L.W.[Lian-Wen],
A New Simplified Gravitational Clustering Method for Multi-prototype Learning Based on Minimum Classification Error Training,
IWICPAS06(168-175).
Springer DOI 0608
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Gedda, M.[Magnus], Svensson, S.[Stina],
Fuzzy Distance Based Hierarchical Clustering Calculated Using the A? Algorithm,
IWCIA06(101-115).
Springer DOI 0606
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Torsello, A.[Andrea], Dowe, D.L.[David L.],
Supervised learning of a generative model for edge-weighted graphs,
ICPR08(1-4).
IEEE DOI 0812
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Torsello, A.[Andrea], Rota Bulo, S.[Samuel], Pelillo, M.[Marcello],
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ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier:
Grouping with Asymmetric Affinities: A Game-Theoretic Perspective,
CVPR06(I: 292-299).
IEEE DOI 0606
BibRef

Kodipaka, S.[Santhosh], Banerjee, A.[Arunava], Vemuri, B.C.[Baba C.],
Large margin pursuit for a Conic Section classifier,
CVPR08(1-6).
IEEE DOI 0806
BibRef
Earlier: A2, A1, A3:
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CVPR06(I: 103-108).
IEEE DOI 0606
Each member class is represented by a conic section, classification by nearness to the conic (using parameteriztion) BibRef

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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Mixture Models, Mixed Pixels .