14.2 Clustering Techniques, Pattern Recognition Techniques

14.2.1 Clustering, Pattern Recognition, General Issues

Presto-Box: Pattern REcognition Scilab TOolBOX,
2001
HTML Version. Code, Pattern Recognition. Routines for students to experiment with basic pattern recognition principles.

PRTools: The Matlab Toolbox for Pattern Recognition,
2004.
WWW Link. Code, Pattern Recognition. Matlab package for PR Implementation based on the book:
See also Classification, parameter estimation and state estimation: An engineering approach using Matlab. 0905

MultiSpec: A Freeware Multispectral Image Data Analysis System,
2007.
WWW Link. Code, Pattern Recognition. Purdue package for analyzing multispectral and hyperspectral data.
See also Purdue University. 0905

Chow, C.K.,
Statistical Independence and Threshold Functions,
TC(14), 1965, pp. 66-68. BibRef 6500

Chow, C., and Liu, C.,
Approximating discrete probability distributions with dependence trees,
IT(14), No. 11, November 1968, pp. 462-467. BibRef 6811

Ho, Y.C., and Kashyap, R.L.,
An Algorithm for Linear Inequalities and its Applications,
TC(14), 1965, pp. 683-688. Decides whether a data set is linearly discriminable and also finds the best discrimination. BibRef 6500

Nadler, M.,
'Empyrean', an alternative paradigm for pattern recognition,
PR(1), No. 2, November 1968, pp. 147-163.
Elsevier DOI 0309
Measurement, decision, feature-extraction. BibRef

Haralick, R.M.,
The Pattern Discrimination Problem from the Perspective of Relation Theory,
PR(7), No. 1-2, June 1975, pp. 67-79.
Elsevier DOI 0309
BibRef

Simon, J.C.,
Recent progress to formal approach of pattern recognition and scene analysis,
PR(7), No. 3, September 1975, pp. 117-124.
Elsevier DOI 0309
Formal descriptions were slow to develop. BibRef

Boullion, T.L., Odell, P.L., Duran, B.S.,
Estimating the probability of misclassification and variate selection,
PR(7), No. 3, September 1975, pp. 139-145.
Elsevier DOI 0309
BibRef

Ripley, B.D.,
The second-order analysis of stationary point processes,
J. Applied Probability(13), 1976, pp. 255-266. BibRef 7600

Ripley, B.D.,
Modelling spatial patterns,
RoyalStat(B-39), 1977, pp. 172-192. BibRef 7700

Ripley, B.D.,
Tests of 'randomness' for spatial point patterns,
RoyalStat(B-41), 1979, pp. 368-374. BibRef 7900

Thiry, S.[Suzanne], Pavel, M.[Monique], Bouckaert, A.[André],
Weighted logical inference for pattern recognition,
PR(12), No. 2, 1980, pp. 63-67.
Elsevier DOI 0309
BibRef

Thiry, S.[Suzanne], Pavel, M.[Monique],
A statistical approach for discrete pattern recognition: A case study in endocrine physiopathology,
PR(18), No. 5, 1985, pp. 349-355.
Elsevier DOI 0309
BibRef

Oja, E.[Erkki], Kuusela, M.[Maija],
The ALSM algorithm: An improved subspace method of classification,
PR(16), No. 4, 1983, pp. 421-427.
Elsevier DOI 0309
BibRef

Laaksonen, J.T.[Jorma T.], and Oja, E.[Erkki],
Density Function Interpretation of Subspace Cassification Methods,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Goldfarb, L.[Lev],
A unified approach to pattern recognition,
PR(17), No. 5, 1984, pp. 575-582.
Elsevier DOI 0309
Within the proposed framework the two principal approaches to pattern recognition (vector and syntactic) are unified. BibRef

Goldfarb, L.[Lev],
On the foundations of intelligent processes--I. An evolving model for pattern learning,
PR(23), No. 6, 1990, pp. 595-616.
Elsevier DOI 0401
BibRef

Wong, S.K.M., Poon, F.C.S.,
Comments on approximating discrete probability distributions with dependence trees,
PAMI(11), No. 3, March 1989, pp. 333-335.
IEEE DOI 0401

See also Approximating discrete probability distributions with dependence trees. BibRef

Avi-Itzhak, H.I., van Mieghem, J.A., Rub, L.,
Multiple Subclass Pattern-Recognition: A Maximum Correlation Approach,
PAMI(17), No. 4, April 1995, pp. 418-431.
IEEE DOI BibRef 9504

Kreutz, M.[Martin], Völpel, B.[Bernd], Janßen, H.[Herbert],
Scale-Invariant Image Recognition Based on Higher-Order Autocorrelation Features,
PR(29), No. 1, January 1996, pp. 19-26.
Elsevier DOI BibRef 9601

Mathieu-Marni, S., Leymarie, P., Berthod, M.,
Removing Ambiguities in a Multispectral Image Classification,
JRS(17), No. 8, May 20 1996, pp. 1493-1504. 9605
BibRef

Yu, F.T.S., Gregory, D.A.,
Optical-Pattern Recognition: Architectures and Techniques,
PIEEE(84), No. 5, May 1996, pp. 733-752. 9605
BibRef

Prakash, M., Murty, M.N.,
Extended Subspace Methods of Pattern-Recognition,
PRL(17), No. 11, September 16 1996, pp. 1131-1139. 9611
BibRef

Prakash, M., Murty, M.N.[M. Narasimha],
Hebbian learning subspace method: A new approach,
PR(30), No. 1, January 1997, pp. 141-149.
Elsevier DOI 0401
BibRef

Prakash, M., Murty, M.N.,
Growing Subspace Pattern-Recognition Methods and Their Neural-Network Models,
TNN(8), No. 1, January 1997, pp. 161-168. 9701
BibRef

Ranka, S.[Sanjay], Heywood, T.[Todd],
Two-Dimensional Pattern Matching with K Mismatches,
PR(24), No. 1, 1991, pp. 31-40.
Elsevier DOI BibRef 9100

Karl, W.C., Kulkarni, S.R., Verghese, G.C., Willsky, A.S.,
Local Tests for Consistency of Support Hyperplane Data,
JMIV(6), No. 2-3, June 1996, pp. 249-267. 9608
BibRef

Ahmed, P., Goyal, P., Narayanan, T.S., Suen, C.Y.,
Linear Time Algorithms for an Image Labelling Machine,
PRL(7), 1988, pp. 273-378. BibRef 8800

Stoyan, D., Molchanov, I.S.,
Set-Valued Means of Random Particles,
JMIV(7), No. 2, March 1997, pp. 111-121.
DOI Link 9705
BibRef

Andres, E., Acharya, R., Sibata, C.,
Discrete Analytical Hyperplanes,
GMIP(59), No. 5, September 1997, pp. 302-309. 9712
BibRef

Talukder, A., Casasent, D.,
General Methodology for Simultaneous Representation and Discrimination of Multiple Object Classes,
OptEng(37), No. 3, March 1998, pp. 904-913. 9804
BibRef

Singh, S.,
2D Spiral Pattern Recognition with Possibilistic Measures,
PRL(19), No. 2, February 1998, pp. 141-147. 9808
BibRef

Sengupta, K.[Kuntal], Boyer, K.L.[Kim L.],
Modelbase Partitioning Using Property Matrix Spectra,
CVIU(70), No. 2, May 1998, pp. 177-196.
DOI Link BibRef 9805
Earlier:
Using Spectral Features for Modelbase Partitioning,
ICPR96(II: 65-69).
IEEE DOI 9608
(Ohio State Univ., USA) BibRef

Chavent, M.[Marie],
A Monothetic Clustering Method,
PRL(19), No. 11, September 1998, pp. 989-996. 9811
BibRef

Cucka, P.[Peter], Netanyahu, N.S.[Nathan S.], Rosenfeld, A.[Azriel],
'Robotic' estimation: the inefficiency of random-walk sampling,
PR(31), No. 12, December 1998, pp. 2091-2102.
Elsevier DOI BibRef 9812

Dybowski, R.[Richard],
Classification of incomplete feature vectors by radial basis function networks,
PRL(19), No. 14, December 1998, pp. 1257-1264. BibRef 9812
And: Erratum: PRL(20), No. 5, May 1999, pp. 549. BibRef

Buczkowski, S.[Stéphane], Kyriacos, S.[Soula], Nekka, F.[Fahima], Cartilier, L.[Louis],
The Modified Box-Counting Method: Analysis of Some Characteristic Parameters,
PR(31), No. 4, April 1998, pp. 411-418.
Elsevier DOI 9803
BibRef

Maharaj, E.A.[Elizabeth Ann],
Comparison and classification of stationary multivariate time series,
PR(32), No. 7, July 1999, pp. 1129-1138.
Elsevier DOI BibRef 9907

Singh, S.[Sameer],
Noise impact on time-series forecasting using an intelligent pattern matching technique,
PR(32), No. 8, August 1999, pp. 1389-1398.
Elsevier DOI BibRef 9908

Singh, S.[Sameer],
Multiple forecasting using local approximation,
PR(34), No. 2, February 2001, pp. 443-455.
Elsevier DOI 0011
BibRef

Singh, S.[Sameer], Stuart, E.[Elizabeth],
A Pattern Matching Tool for Time-Series Forecasting,
ICPR98(Vol I: 103-105).
IEEE DOI 9808
BibRef

Hess, C.[Christian],
Conditional expectation and martingales of random sets,
PR(32), No. 9, September 1999, pp. 1543-1567.
Elsevier DOI BibRef 9909

Sidiropoulos, N.D.,
On the tractability of estimating the germ process of certain germ-grain random set models and related problems,
PR(32), No. 9, September 1999, pp. 1667-1674.
Elsevier DOI BibRef 9909

Last, G., Holtmann, M.,
On the empty space function of some germ-grain models,
PR(32), No. 9, September 1999, pp. 1587-1600.
Elsevier DOI BibRef 9909

Balachander, T.[Thiagarajan], Kothari, R.[Ravi],
Introducing Locality and Softness in Subspace Classification,
PAA(2), No. 1, 1999, pp. 53-58. BibRef 9900

Comaniciu, D.[Dorin], Meer, P.[Peter],
Distribution Free Decomposition of Multivariate Data,
PAA(2), No. 1, 1999, pp. 22-30. BibRef 9900

Mitra, S.K.[Suman K.], Murthy, C.A.,
Mathematical framework to show the existence of attractor of partitioned iterative function systems,
PR(33), No. 5, May 2000, pp. 859-869.
Elsevier DOI 0003
BibRef

Ruiz Vargas, J.A., Hemerly, E.M.,
Adaptive observers for unknown general nonlinear systems,
SMC-B(31), No. 5, October 2001, pp. 683-690.
IEEE Top Reference. 0111
BibRef

Hastie, T., Tibshirani, R., Friedman, J.,
The Elements of Statistical Learning: Data Mining, Inference and Prediction,
Springer-Verlag2001. BibRef 0100

Duin, R.P.W.[Robert P.W.], Roli, F.[Fabio], de Ridder, D.[Dick],
A note on core research issues for statistical pattern recognition,
PRL(23), No. 4, February 2002, pp. 493-499.
Elsevier DOI 0202
BibRef

Szczecinski, L.L.[Leszek L.], Gei, A.[Ado],
Blind decision feedback equalisers, how to avoid degenerative solutions,
SP(82), No. 11, November 2002, pp. 1675-1693.
Elsevier DOI 0210
BibRef

Serpico, S.B., Moser, G.,
Weight Parameter Optimization by the Ho-Kashyap Algorithm in MRF Models for Supervised Image Classification,
GeoRS(44), No. 12, December 2006, pp. 3695-3705.
IEEE DOI 0701

See also Algorithm for Linear Inequalities and its Applications, An. BibRef

Haralick, R.M., Miasnikov, A.[Alex], and Myasnikov, A.D.[Alexei D.],
Pattern Recognition Approaches To Solving Combinatorial Problems in Free Groups,
Contemporary Mathematics(349), 2004, pp. 197-213. BibRef 0400

Haralick, R.M., Miasnikov, A.[Alex], and Myasnikov, A.D.[Alexei D.],
Heuristics for Whitehead Minimization Problem,
Experimental Mathematics(14), No. 1, 2005, pp. 7-14. BibRef 0500

Haralick, R.M., Myasnikov, A.D.[Alexei D.],
A Hybrid Algorithm For Solving the Whitehead Minimization Problem,
Symbolic Computation(41), No. 7, July 2006, pp. 818-834. BibRef 0607

van der Heijden, F.[Ferdi], Duin, R.P.W.[Robert P.W.], de Ridder, D.[Dick], Tax, a.D.M.J.[and David M.J.],
Classification, parameter estimation and state estimation: An engineering approach using Matlab,
John Wileyand Sons, 2004. ISBN 0470090138. Buy this book: Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB For code:
See also PRTools: The Matlab Toolbox for Pattern Recognition. 0905
BibRef

Owczarczuk, M.[Marcin],
New separating hyperplane method with application to the optimisation of direct marketing campaigns,
PRL(32), No. 3, 1 February 2011, pp. 540-545.
Elsevier DOI 1101
Separating hyperplane; Vapnik-Chervonenkis dimension; Direct marketing; Lift curve BibRef

Merkurjev, E., Kostic, T., Bertozzi, A.L.[Andrea L.],
An MBO Scheme on Graphs for Classification and Image Processing,
SIIMS(6), No. 4, 2013, pp. 1903-1930.
DOI Link 1402
MBO: Merriman-Bence-Osher. BibRef

Merkurjev, E.[Ekaterina], Bae, E.[Egil], Bertozzi, A.L.[Andrea L.], Tai, X.C.[Xue-Cheng],
Global Binary Optimization on Graphs for Classification of High-Dimensional Data,
JMIV(52), No. 3, July 2015, pp. 414-435.
Springer DOI 1506
BibRef

Peters, G.[Georg],
Is there any need for rough clustering?,
PRL(53), No. 1, 2015, pp. 31-37.
Elsevier DOI 1502
Rough clustering BibRef

Hasan, M.M.[M. Mahbubul], Islam, A.S.M.S.[A.S.M. Shohidull], Rahman, M.S.[Mohammad Saifur], Rahman, M.S.[M. Sohel],
Order preserving pattern matching revisited,
PRL(55), No. 1, 2015, pp. 15-21.
Elsevier DOI 1503
Algorithms. OPPM. BibRef

Vaiciukynas, E., Ulicny, M., Pashami, S., Nowaczyk, S.,
Learning Low-Dimensional Representation of Bivariate Histogram Data,
ITS(19), No. 11, November 2018, pp. 3723-3735.
IEEE DOI 1812
data mining, intelligent transportation systems, mean square error methods, pattern classification, autoencoder. BibRef

Aksac, A.[Alper], Özyer, T.[Tansel], Alhajj, R.[Reda],
CutESC: Cutting edge spatial clustering technique based on proximity graphs,
PR(96), 2019, pp. 106948.
Elsevier DOI 1909
Spatial data mining, Clustering, Proximity graphs, Graph theory BibRef

Wang, J.C.[Jia-Chun], Sun, S.L.[Shi-Liang],
Decomposed slice sampling for factorized distributions,
PR(97), 2020, pp. 107021.
Elsevier DOI 1910
Slice sampling, Markov chain Monte Carlo, Decomposed slice sampling, Hamiltonian Monte Carlo BibRef

Gu, B.[Bin], Ling, C.X.[Charles X.],
Generalized error path algorithm,
PR(120), 2021, pp. 108112.
Elsevier DOI 2109
Cross validation, Error path, Solution path, Model selection BibRef

Liu, H.T.[Hai-Tao], Ong, Y.S.[Yew-Soon], Jiang, X.M.[Xiao-Mo], Wang, X.F.[Xiao-Fang],
Modulating scalable Gaussian processes for expressive statistical learning,
PR(120), 2021, pp. 108121.
Elsevier DOI 2109
Gaussian process, Modulation, Scalability, Heteroscedastic noise, Multi-modality, Non-stationarity BibRef

Vovk, V.[Vladimir],
Universal predictive systems,
PR(126), 2022, pp. 108536.
Elsevier DOI 2204
Conformal prediction, Predictive distribution, Probabilistic calibration, Universal consistency BibRef

Qv, H.[Hui], Ma, T.[Tao], Tong, X.Y.[Xin-Yi], Huang, X.[Xuhui], Ma, Z.[Zhe], Feng, J.H.[Jie-Hong],
Clustering by centroid drift and boundary shrinkage,
PR(129), 2022, pp. 108745.
Elsevier DOI 2206
Clustering, Centroid drift, Boundary detection BibRef

Xue, C.[Cheng], Yu, L.[Lequan], Chen, P.F.[Peng-Fei], Dou, Q.[Qi], Heng, P.A.[Pheng-Ann],
Robust Medical Image Classification From Noisy Labeled Data With Global and Local Representation Guided Co-Training,
MedImg(41), No. 6, June 2022, pp. 1371-1382.
IEEE DOI 2206
Noise measurement, Training, Biomedical imaging, Medical diagnostic imaging, Training data, Deep learning, self-supervision BibRef

Gong, C.[Chen], Wang, Q.Z.[Qi-Zhou], Liu, T.L.[Tong-Liang], Han, B.[Bo], You, J.[Jane], Yang, J.[Jian], Tao, D.C.[Da-Cheng],
Instance-Dependent Positive and Unlabeled Learning With Labeling Bias Estimation,
PAMI(44), No. 8, August 2022, pp. 4163-4177.
IEEE DOI 2207
Labeling, Maximum likelihood estimation, Training, Random variables, Logistics, Graphical models, Data models, generalization bound BibRef

Cheng, D.[De], Liu, T.L.[Tong-Liang], Ning, Y.X.[Yi-Xiong], Wang, N.N.[Nan-Nan], Han, B.[Bo], Niu, G.[Gang], Gao, X.B.[Xin-Bo], Sugiyama, M.[Masashi],
Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation,
CVPR22(16609-16618)
IEEE DOI 2210
Representation learning, Manifolds, Geometry, Estimation error, Psychology, Physiology, Representation learning, Self- semi- meta- unsupervised learning BibRef

Zhu, Z.W.[Zhao-Wei], Liu, T.L.[Tong-Liang], Liu, Y.[Yang],
A Second-Order Approach to Learning with Instance-Dependent Label Noise,
CVPR21(10108-10118)
IEEE DOI 2111

WWW Link. Training, Deep learning, Estimation, Rendering (computer graphics), Pattern recognition, Task analysis BibRef

Zheng, Z.C.[Zhi-Chao], Sun, H.J.[Huai-Jiang], Zhou, Y.[Ying],
Margin embedding net for robust margin collaborative representation-based classification,
PR(133), 2023, pp. 108991.
Elsevier DOI 2210
Collaborative representation, Feature extraction, Marginal sample, Image classification BibRef

Hirschberger, F.[Florian], Forster, D.[Dennis], Lücke, J.[Jörg],
A Variational EM Acceleration for Efficient Clustering at Very Large Scales,
PAMI(44), No. 12, December 2022, pp. 9787-9801.
IEEE DOI 2212
Clustering algorithms, Data models, Optimization, Task analysis, Standards, Approximation algorithms, Training, Big data, clustering, variational optimization BibRef

Zhang, Q.H.[Qing-Hua], Dai, Y.Y.[Yong-Yang], Wang, G.Y.[Guo-Yin],
Density peaks clustering based on balance density and connectivity,
PR(134), 2023, pp. 109052.
Elsevier DOI 2212
Clustering, Mutual nearest neighbor, Connectivity between data points, Fast search strategy BibRef

Pei, S.F.[Shen-Fei], Chen, H.M.[Hui-Min], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Li, X.L.[Xue-Long],
Centerless Clustering,
PAMI(45), No. 1, January 2023, pp. 167-181.
IEEE DOI 2212
Clustering algorithms, Clustering methods, Analytical models, Computational modeling, Symmetric matrices, Linear programming, k-means BibRef

Sagar, K.[Ksheera], Bhadra, A.[Anindya],
A Laplace Mixture Representation of the Horseshoe and Some Implications,
SPLetters(29), 2022, pp. 2547-2551.
IEEE DOI 2301
Mathematical models, Optimization, Linear approximation, Random variables, Estimation, Convergence, Tail, local linear approximation BibRef

Xu, C.J.[Cheng-Jun], Shu, J.Q.[Jing-Qian], Zhu, G.B.[Guo-Bin],
Scene Classification Based on Heterogeneous Features of Multi-Source Data,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Zhao, J.[Jia], Wang, G.[Gang], Pan, J.S.[Jeng-Shyang], Fan, T.H.[Tang-Huai], Lee, I.[Ivan],
Density peaks clustering algorithm based on fuzzy and weighted shared neighbor for uneven density datasets,
PR(139), 2023, pp. 109406.
Elsevier DOI 2304
Uneven density data, Density peaks clustering, Fuzzy neighborhood, K-nearest neighbor, Weighted shared neighbor BibRef

Hofmeyr, D.P.[David P.],
Incremental estimation of low-density separating hyperplanes for clustering large data sets,
PR(139), 2023, pp. 109471.
Elsevier DOI 2304
Clustering, Low-density separation, Big data, Stochastic gradient descent, Smoothing kernel, High dimensionality BibRef

He, Q.[Qian], Yu, F.[Fusheng], Chang, J.Q.[Jia-Qi], Ouyang, C.X.[Chen-Xi],
Fuzzy granular recurrence plot and quantification analysis: A novel method for classification,
PR(139), 2023, pp. 109456.
Elsevier DOI 2304
Fuzzy granular recurrence plot, Recurrence plot, Fuzzy information granule, Noise, Classification BibRef

Alfaro, J.C.[Juan C.], Aledo, J.A.[Juan A.], Gámez, J.A.[José A.],
Pairwise learning for the partial label ranking problem,
PR(140), 2023, pp. 109590.
Elsevier DOI 2305
Preference learning, (Partial) label ranking, Supervised classification, Pairwise decomposition, Optimal bucket order problem BibRef

Li, S.J.[Shao-Jie], Liu, Y.[Yong],
Learning Rates for Nonconvex Pairwise Learning,
PAMI(45), No. 8, August 2023, pp. 9996-10011.
IEEE DOI 2307
Convergence, Stability analysis, Measurement, Training, Statistics, Sociology, Optimization, Generalization performance, pairwise learning BibRef

Ollila, E.[Esa], Palomar, D.P.[Daniel P.], Pascal, F.[Frédéric],
Affine Equivariant Tyler's M-Estimator Applied to Tail Parameter Learning of Elliptical Distributions,
SPLetters(30), 2023, pp. 1017-1021.
IEEE DOI 2309
BibRef

Kang, H.H.[Hyun Ho], Ahn, C.K.[Choon Ki],
Neural Network-Based Moving Window Iterative Nonlinear System Identification,
SPLetters(30), 2023, pp. 1007-1011.
IEEE DOI 2309
BibRef

Rezaei, H.[Hamid], Daneshpour, N.[Negin],
Mixed data clustering based on a number of similar features,
PR(143), 2023, pp. 109815.
Elsevier DOI 2310
Clustering, Mixed data, Data object distance, Similarity, Cluster center BibRef

Huang, Y.C.[Ying-Cheng], Xiao, F.Y.[Fu-Yuan], Cao, Z.H.[Ze-Hong], Lin, C.T.[Chin-Teng],
Higher Order Fractal Belief Rényi Divergence With Its Applications in Pattern Classification,
PAMI(45), No. 12, December 2023, pp. 14709-14726.
IEEE DOI 2311
BibRef

Hu, X.B.[Xiao-Bo], Su, J.B.[Jian-Bo], Zhang, J.[Jun],
Disturbance rejection with compensation on features,
PR(147), 2024, pp. 110129.
Elsevier DOI 2312
Compensation, Disturbance rejection, Modeling error BibRef

Saltos, R.[Ramiro], Weber, R.[Richard],
Generalized black hole clustering algorithm,
PRL(176), 2023, pp. 196-201.
Elsevier DOI 2312
Inspired by the Density-based Spatial Clustering of Applications with Noise (DBSCAN). Density-based clustering, Pattern recognition, Data mining BibRef

Breger, A.[Anna], Karner, C.[Clemens], Ehler, M.[Martin],
visClust: A visual clustering algorithm based on orthogonal projections,
PR(148), 2024, pp. 110136.
Elsevier DOI Code:
WWW Link. 2402
Clustering, Data mining, Stiefel manifold, Lower dimensional data representations, Projections BibRef

Alexandre, L.[Leonardo], Costa, R.S.[Rafael S.], Henriques, R.[Rui],
TriSig: Evaluating the statistical significance of triclusters,
PR(149), 2024, pp. 110231.
Elsevier DOI 2403
Triclustering, Pattern discovery, Statistical significance, Temporal pattern mining, Multivariate time series data BibRef

Zhang, L.[Lu], Qi, L.[Lu], Yang, X.[Xu], Qiao, H.[Hong], Yang, M.H.[Ming-Hsuan], Liu, Z.Y.[Zhi-Yong],
Automatically Discovering Novel Visual Categories With Adaptive Prototype Learning,
PAMI(46), No. 4, April 2024, pp. 2533-2544.
IEEE DOI 2403
Task analysis, Prototypes, Feature extraction, Data models, Self-supervised learning, Training, Representation learning, transfer learning BibRef

Rubaiyat, A.H.M.[Abu Hasnat Mohammad], Li, S.Y.[Shi-Ying], Yin, X.[Xuwang], Shifat-E-Rabbi, M.[Mohammad], Zhuang, Y.[Yan], Rohde, G.K.[Gustavo K.],
End-to-End Signal Classification in Signed Cumulative Distribution Transform Space,
PAMI(46), No. 9, September 2024, pp. 5936-5950.
IEEE DOI Code:
WWW Link. 2408
Transforms, Feature extraction, Time series analysis, Pattern classification, Data models, nearest local subspace BibRef

Motallebi, H.[Hassan],
Efficient and robust clustering based on backbone identification,
PR(155), 2024, pp. 110635.
Elsevier DOI 2408
Clustering algorithm, Data backbone, Popularity, Popularity-based clustering, Arbitrary-shaped clusters BibRef

Wang, J.H.[Jin-Hong], Cheng, Y.[Yi], Chen, J.T.[Jin-Tai], Chen, T.T.[Ting-Ting], Chen, D.[Danny], Wu, J.[Jian],
Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction,
ICCV23(5842-5852)
IEEE DOI Code:
WWW Link. 2401
medical disease grading and movie rating. BibRef

Ding, T.J.[Tian-Jiao], Tong, S.B.[Sheng-Bang], Chan, K.H.R.[Kwan Ho Ryan], Dai, X.[Xili], Ma, Y.[Yi], Haeffele, B.D.[Benjamin D.],
Unsupervised Manifold Linearizing and Clustering,
ICCV23(5427-5438)
IEEE DOI 2401
BibRef

Nápoles, G.[Gonzalo], Griffioen, N.[Niels], Khoshrou, S.[Samaneh], Güven, Ç.[Çiçek],
Feature Importance for Clustering,
CIARP23(I:31-45).
Springer DOI 2312
BibRef

Zhang, J.M.[Jia-Ming], Ma, X.[Xingjun], Yi, Q.[Qi], Sang, J.[Jitao], Jiang, Y.G.[Yu-Gang], Wang, Y.[Yaowei], Xu, C.S.[Chang-Sheng],
Unlearnable Clusters: Towards Label-Agnostic Unlearnable Examples,
CVPR23(3984-3993)
IEEE DOI 2309
BibRef

Cunningham, J.[James], Davis, J.[Jim], Tarplee, K.[Kyle], Vasquez, J.[Juan],
S-FINCH: An Optimized Streaming Adaptation to FINCH Clustering,
ICPR22(1343-1349)
IEEE DOI 2212
Sensitivity, Heuristic algorithms, Clustering methods, Soft sensors, Clustering algorithms, Data science, Benchmark testing BibRef

Rambhatla, S.S.[Sai Saketh], Chellappa, R.[Rama], Shrivastava, A.[Abhinav],
The Pursuit of Knowledge: Discovering and Localizing Novel Categories using Dual Memory,
ICCV21(9133-9143)
IEEE DOI 2203
Semantics, Focusing, Memory modules, Detectors, Transfer/Low-shot/Semi/Unsupervised Learning, Detection and localization in 2D and 3D BibRef

Jia, X.[Xuhui], Han, K.[Kai], Zhu, Y.K.[Yu-Kun], Green, B.[Bradley],
Joint Representation Learning and Novel Category Discovery on Single- and Multi-Modal Data,
ICCV21(590-599)
IEEE DOI 2203
Training, Representation learning, Estimation, Clustering algorithms, Benchmark testing, Prediction algorithms, Vision applications and systems BibRef

Afser, H.[Hüseyin],
Statistical Classification via Robust Hypothesis Testing: Non-Asymptotic and Simple Bounds,
SPLetters(28), 2021, pp. 2112-2116.
IEEE DOI 2112
Training, Upper bound, Testing, Error probability, Bayes methods, Complexity theory, Task analysis, Statistical classification, DGL test BibRef

Charoenphakdee, N.[Nontawat], Vongkulbhisal, J.[Jayakorn], Chairatanakul, N.[Nuttapong], Sugiyama, M.[Masashi],
On Focal Loss for Class-Posterior Probability Estimation: A Theoretical Perspective,
CVPR21(5198-5207)
IEEE DOI 2111
Estimation, Object detection, Minimization, Pattern recognition, Calibration, Image classification BibRef

Lawson, A.[Austin], Chung, Y.M.[Yu-Min], Cruse, W.[William],
A Hybrid Metric based on Persistent Homology and its Application to Signal Classification,
ICPR21(9944-9950)
IEEE DOI 2105
Shape of the data. Measurement, Weight measurement, Data analysis, Shape, Time series analysis, Transforms, Benchmark testing BibRef

Bai, J.[Jing], Chen, R.[Ran],
Context-Aware Residual Module for Image Classification,
ICPR21(3388-3395)
IEEE DOI 2105
Visualization, Image recognition, Semantics, Focusing, Data mining, Task analysis, Image classification, context-aware, multi-scale, image classificaiton BibRef

Laroui, S.[Sarah], Descombes, X.[Xavier], Vernay, A.[Aurélia], Villiers, F.[Florent], Villalba, F.[François], Debreuve, E.[Eric],
How to define a rejection class based on model learning?,
ICPR21(569-576)
IEEE DOI 2105
Computational modeling, Probability density function, Pattern recognition BibRef

Ayma, V.A., Ferreira, R.S., Happ, P., Oliveira, D., Feitosa, R., Costa, G., Plaza, A., Gamba, P.,
Classification Algorithms for Big Data Analysis, A Map Reduce Approach,
PIA15(17-21).
DOI Link 1504
BibRef

Lovato, P.[Pietro], Milanese, A.[Alessio], Centomo, C.[Cesare], Giorgetti, A.[Alejandro], Bicego, M.[Manuele],
S-BLOSUM: Classification of 2D Shapes with Biological Sequence Alignment,
ICPR14(2335-2340)
IEEE DOI 1412
Accuracy BibRef

Wang, X.Y.[Xiao-Yang], Ji, Q.A.[Qi-Ang],
A Unified Probabilistic Approach Modeling Relationships between Attributes and Objects,
ICCV13(2120-2127)
IEEE DOI 1403
for attribute prediction and object recognition. BibRef

Glazer, A.[Assaf], Lindenbaum, M.[Michael], Markovitch, S.[Shaul],
Feature shift detection,
ICPR12(1383-1386).
WWW Link. 1302
hidden changes due to feature value differences. BibRef

Muñoz, A.[Alberto], González, J.[Javier],
Combining Functional Data Projections for Time Series Classification,
CIARP09(457-464).
Springer DOI 0911
kernel Hilbert space. BibRef

Pérez-Bonilla, A.[Alejandra], Gibert, K.[Karina],
Towards Automatic Generation of Conceptual Interpretation of Clustering,
CIARP07(653-663).
Springer DOI 0711
BibRef

Hammer, R.[Rubi], Hertz, T.[Tomer], Hochstein, S.[Shaul], Weinshall, D.[Daphna],
Classification with Positive and Negative Equivalence Constraints: Theory, Computation and Human Experiments,
BVAI07(264-276).
Springer DOI 0710
BibRef

Agarwal, S.[Sameer], Lim, J.W.[Jong-Woo], Zelnik-Manor, L.[Lihi], Perona, P.[Pietro], Kriegman, D.J.[David J.], Belongie, S.J.[Serge J.],
Beyond Pairwise Clustering,
CVPR05(II: 838-845).
IEEE DOI 0507
Relations are not pairwise, but 3, 4 or more. Instance of hypergraph partitioning problem. BibRef

Altmueller, S., Haralick, R.M.,
Approximating high dimensional probability distributions,
ICPR04(II: 299-302).
IEEE DOI 0409
Compare to
See also Approximating discrete probability distributions with dependence trees. BibRef

Sanders, B.C.S., Nelson, R.C., Sukthankar, R.[Rahul],
A theory of the quasi-static world,
ICPR02(III: 1-6).
IEEE DOI 0211
BibRef

Mottl, V., Dvoenko, S., Kopylov, A.,
Pattern Recognition in Interrelated Data: The Problem, Fundamental Assumptions, Recognition Algorithms,
ICPR04(I: 188-191).
IEEE DOI 0409
BibRef

Mottl, V., Seredin, O., Dvoenko, S., Kulikowski, C., Muchnik, I.,
Featureless pattern recognition in an imaginary Hilbert space,
ICPR02(II: 88-91).
IEEE DOI 0211
BibRef

Singh, S., Galton, A.,
Pattern recognition using information slicing method (PRISM),
ICPR02(II: 144-147).
IEEE DOI 0211
BibRef

Saalbach, A., Heidemann, G., Ritter, H.,
Representing object manifolds by parametrized SOMs,
ICPR02(II: 184-187).
IEEE DOI 0211
BibRef

Ohta, Y.,
Pattern recognition and understanding for visual information media,
ICPR02(I: 536-545).
IEEE DOI 0211
BibRef

Balthasar, D., Priese, L.,
Fast projection plane classifier,
ICPR02(II: 200-203).
IEEE DOI 0211
BibRef

Ryazanov, V.V., Vorontchikhin, V.A.,
Discrete approach for automatic knowledge extraction from precedent large-scale data, and classification,
ICPR02(II: 188-191).
IEEE DOI 0211
BibRef

Veeramachaneni, S., Fujisawa, H., Liu, C.L.[Cheng-Lin], Nagy, G.,
Classifying isogenous fields,
FHR02(41-46).
IEEE Top Reference. 0209
BibRef

Bax, E.,
Using Validation by Inference to Select a Hypothesis Function,
ICPR00(Vol II: 700-703).
IEEE DOI 0009
BibRef

Amengual, J.C., Vidal, E.,
On the Estimation of Error-correcting Parameters,
ICPR00(Vol II: 883-886).
IEEE DOI BibRef 0001 ICPR00(Vol II: 887-890).
IEEE DOI 0009
BibRef

Baram, Y.,
Random Embedding Machines for Low-complexity Pattern Recognition,
ICPR00(Vol II: 748-754).
IEEE DOI 0009
BibRef

Law, M.H., Kwok, J.T.,
Rival Penalized Competitive Learning for Model-based Sequence Clustering,
ICPR00(Vol II: 195-198).
IEEE DOI 0009
BibRef

Matas, J.G.[Jiri G.], Pandit, M., Kittler, J.V.[Josef V.],
Selection of Speaker Independent Feature for a Speaker Verification System,
ICPR98(Vol II: 1034-1036).
IEEE DOI 9808
BibRef

Riazanov, V.V.[Vladimir V.], Sen'ko, O.V., Zhuralvlev, Y.I.[Yu I.],
Mathematical Methods for Pattern Recognition: Logic, Optimization, Algebraic Approaches,
ICPR98(Vol I: 831-834).
IEEE DOI 9808
BibRef

Olivier, C., Jouzel, F., Avila, M.,
Markov Model Order Optimization for Text Recognition,
ICDAR97(548-551).
IEEE DOI 9708
BibRef

Gennert, M.A., Yuille, A.L.,
Determining the Optimal Weights in Multiple Objective Function Optimization,
ICCV88(87-89).
IEEE DOI BibRef 8800

Lemaire, J., Barrouil, C.,
Use of a priori descriptions in a high-level language and management of the uncertainty in a scene recognition system,
ICPR96(I: 560-564).
IEEE DOI 9608
(Centre d`Etudes et de Recherches, F) BibRef

Lemaire, J., Le Moigne, O.,
Development of a scene recognition system with imprecise descriptions,
ICIP96(II: 979-982).
IEEE DOI 9610
BibRef

Kimura, F., Miyake, Y., Wakabayashi, T.,
On Feature Extraction for Limited Class Problem,
ICPR96(II: 191-194).
IEEE DOI 9608
(Mie Univ., J) BibRef

Dzemyda, G.,
Visual Analysis of a Set of Function Values,
ICPR96(II: 700-704).
IEEE DOI 9608
(Institute of Mathematics and Informatics, LIT) BibRef

Uhl, C., Friedrich, R.,
Spatiotemporal Signal Analysis: Recognition of Interacting Modes,
ICPR96(II: 55-59).
IEEE DOI 9608
(Max-Planck-Institute, D) BibRef

Arumugavelu, S., Ranganathan, N.,
SIMD Algorithms for Single Link and Complete Link Pattern Clustering,
ICPR96(IV: 625-629).
IEEE DOI 9608
(Univ. of Florida, USA) BibRef

Blyumin, S.L.,
Multiplicative bases approach in mathematical cybernetics,
ICPR94(B:550-552).
IEEE DOI 9410
BibRef

Chen, Y.S.[Yung-Sheng], Shao, W.S.[Wei-Shin],
Useful information plane on pattern classification,
ICPR94(B:605-607).
IEEE DOI 9410
BibRef

Howard, C.G.[Cheryl G.], Bock, P.[Peter],
Multi-class classification and symbolic cognitive processing with ALISA,
CAIP93(343-354).
Springer DOI 9309
BibRef

Lazar, C.,
Pattern recognition algorithm based on cyclic codes,
ICPR92(II:455-457).
IEEE DOI 9208
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Clustering, Classification, General Methods .