14.2.7 Distance Measures, Criteria for Clustering

Chapter Contents (Back)
Discrimination Rule. Distance Measures. Similarity. Dissimilarity 9805

See also Distance Transforms, Distance Functions, Distance Measures.
See also Similarity Measure, Distance Transforms and Functions for Objects and Shapes.
See also Three Dimensional Distance Transforms and Distance Functions.
See also Distance Transforms, Functions and Skeletons.
See also Graph Clustering, Cilque Generation.

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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
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Popovici, V.[Vlad], Bengio, S.[Samy], Thiran, J.P.[Jean-Philippe],
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PR(38), No. 12, December 2005, pp. 2385-2390.
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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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Smyth, C.[Christine], Coomans, D.[Danny], Everingham, Y.[Yvette],
Clustering noisy data in a reduced dimension space via multivariate regression trees,
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.[Torbjřrn],
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
BibRef

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
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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.[Adriăo Dória], Martins, A.[Allan],
Representative cross information potential clustering,
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Clustering. Interactions between distributions. BibRef

Moreno, R.[Rodrigo], Koppal, S.[Sandeep], de Muinck, E.[Ebo],
Robust estimation of distance between sets of points,
PRL(34), No. 16, 2013, pp. 2192-2198.
Elsevier DOI 1310
Spatial statistics BibRef

Yu, Y.W.[Ying-Wei], Gutierrez-Osuna, R.[Ricardo], Choe, Y.[Yoonsuck],
Context-sensitive intra-class clustering,
PRL(37), No. 1, 2014, pp. 85-93.
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,
ICCV09(48-55).
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

Kobayashi, T.[Takumi],
Low-Rank Bilinear Classification: Efficient Convex Optimization and Extensions,
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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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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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Kobayashi, T.[Takumi],
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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],
Aggregation distance measure and its induced similarity measure between intuitionistic fuzzy sets,
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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],
Evaluation of ground distances and features in EMD-based GMM matching for texture classification,
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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],
Nearest neighbour estimators of density derivatives, with application to mean shift clustering,
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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

Chen, M.[Mei], Li, L.J.[Long-Jie], Wang, B.[Bo], Cheng, J.J.[Jian-Jun], Pan, L.[Lina], Chen, X.Y.[Xiao-Yun],
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,
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Elsevier DOI 1708
Ambiguity resolution. Datapoints have multiple labels. BibRef

Ortakaya, A.F.[Ahmet Fatih],
Independently weighted value difference metric,
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Elsevier DOI 1709
Categorical classification BibRef

Thorpe, M.[Matthew], Park, S.[Serim], Kolouri, S.[Soheil], Rohde, G.K.[Gustavo K.], Slepcev, D.[Dejan],
A Transportation Lp Distance for Signal Analysis,
JMIV(59), No. 2, October 2017, pp. 187-210.
Springer DOI 1709
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Crook, O.M.[Oliver M.], Cucuringu, M.[Mihai], Hurst, T.[Tim], Schönlieb, C.B.[Carola-Bibiane], Thorpe, M.[Matthew], Zygalakis, K.C.[Konstantinos C.],
A linear transportation Lp distance for pattern recognition,
PR(147), 2024, pp. 110080.
Elsevier DOI 2312
Optimal transport, Linear embedding, Multi-channelled signals BibRef

Bai, Y.K.[Yi-Kun], Schmitzer, B.[Bernhard], Thorpe, M.[Matthew], Kolouri, S.[Soheil],
Sliced Optimal Partial Transport,
CVPR23(13681-13690)
IEEE DOI 2309
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Park, S.[Serim], Thorpe, M.[Matthew],
Representing and Learning High Dimensional Data with the Optimal Transport Map from a Probabilistic Viewpoint,
CVPR18(7864-7872)
IEEE DOI 1812
Strain, Measurement, Manifolds, Probabilistic logic, Data models, Face, Deformable models 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
BibRef

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.[Younčs], 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.[Qiaolin], 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.[Ziwen], 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


Park, D.[Dogyun], Kim, S.[Suhyun],
Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models,
ICCV23(20042-20052)
IEEE DOI Code:
WWW Link. 2401
BibRef

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
BibRef

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).
WWW Link. 1302
for 2-class problems. BibRef

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).
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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-).
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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
BibRef

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).
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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).
Springer DOI 0911
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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).
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Dehzangi, O.[Omid], Zolghadri, M.J.[Mansoor J.], Taheri, S.[Shahram], Dehzangi, A.[Abdollah],
An Efficient Nearest Neighbor Classifier Using an Adaptive Distance Measure,
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

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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.[Xichen], 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
Earlier:
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],
Beyond partitions: Allowing overlapping groups in pairwise clustering,
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:
A Conic Section Classifier and its Application to Image Datasets,
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

Joachims, T.[Thorsten],
The Maximum Margin Approach to Learning Text Classifiers Methods, Theory and Algorithms,
Ph.D.thesis, Dortmund Univ., 2000. BibRef 0001

Toh, K.A.[Kar-Ann], Jiang, X.D.[Xu-Dong], Yau, W.Y.[Wei-Yun],
Relaxation of Hard Classification Targets for LSE Minimization,
EMMCVPR05(187-202).
Springer DOI 0601
BibRef

Polat, K.[Kemal], Sahan, S.[Seral], Kodaz, H.[Halife], Günes, S.[Salih],
Outdoor Image Classification Using Artificial Immune Recognition System (AIRS) with Performance Evaluation by Fuzzy Resource Allocation Mechanism,
CAIP05(81).
Springer DOI 0509
BibRef

Zhang, W.D.[Wen-De], Chen, T.H.[Tsu-Han],
Classification based on symmetric maximized minimal distance in subspace (SMMS),
CVPR03(II: 100-105).
IEEE DOI 0307
BibRef

Yang, M.H.[Ming-Hsuan],
Extended Isomap for classification,
ICPR02(III: 615-618).
IEEE DOI 0211
BibRef

Muller, N., Herbst, B.M.,
Extending the linear interpolating condition to advanced synthetic discriminant function variants,
ICPR02(III: 883-886).
IEEE DOI 0211

See also On the Use of SDF-Type Filters for Distortion Parameter Estimation. BibRef

Zhu, Y.[Ying], Schwartz, S.,
Discriminant analysis and adaptive wavelet feature selection for statistical object detection,
ICPR02(IV: 86-89).
IEEE DOI 0211
BibRef

Ibrahimov, O., Sethi, I., Dimitrova, N.,
The performance analysis of a chi-square similarity measure for topic related clustering of noisy transcripts,
ICPR02(IV: 285-288).
IEEE DOI 0211
BibRef

Ujiie, H., Omachi, S., Aso, H.,
A discriminant function considering normality improvement of the distribution,
ICPR02(II: 224-227).
IEEE DOI 0211
BibRef

Chernov, V.M.[Vladimir M.],
Diophantine Approximations of Algebraic Irrationalities and Stability Theorems for Polynomial Decision Rules,
CAIP01(177 ff.).
Springer DOI 0210
BibRef

Barla, A., Odone, F.[Francesca], Verri, A.[Alessandro],
Histogram intersection kernel for image classification,
ICIP03(III: 513-516).
IEEE DOI 0312
BibRef

Barla, A., Odone, F., Verri, A.,
Hausdorff Kernel for 3D Object Acquisition and Detection,
ECCV02(IV: 20 ff.).
Springer DOI 0205
BibRef

Barla, A.[Annalisa], Odone, F., Verri, A.,
Old fashioned state-of-the-art image classification,
CIAP03(566-571).
IEEE DOI 0310
BibRef

Ménard, M., Dardignac, P.A., Courboulay, V.,
Switching Regression Models Using Ambiguity and Distance Rejects: Application to Ionogram Analysis,
ICPR00(Vol II: 688-691).
IEEE DOI 0009
BibRef

Keysers, D., Dahmen, J., Theiner, T., Ney, H.,
Experiments with an Extended Tangent Distance,
ICPR00(Vol II: 38-42).
IEEE DOI 0009
BibRef

Foucher, S., Boucher, J.M., Benie, G.B.,
Multiscale and Multisource Classification using Dempster-Shafer Theory,
ICIP99(I:124-128).
IEEE DOI BibRef 9900

Anh, V., Bui, T., Chen, G., and Tieng, Q.,
The Hellinger-Kakutani Metric for Pattern Recognition,
ICIP97(II: 430-433).
IEEE DOI BibRef 9700

Murthy, C.A., Majumder, D.D.,
A method for consistent estimation of compact regions for cluster analysis,
ICPR90(I: 665-667).
IEEE DOI 9006
BibRef

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


Last update:Mar 16, 2024 at 20:36:19