14.2.7 Distance Measures, Criteria for Clustering

Chapter Contents (Back)
Discrimination Rule. Distance Measures. 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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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.
WWW Link. 0510
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Popovici, V.[Vlad], Bengio, S.[Samy], Thiran, J.P.[Jean-Philippe],
Kernel matching pursuit for large datasets,
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.
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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.
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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.
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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.
WWW Link. 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
BibRef

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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 0611
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Earlier:
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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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
BibRef

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
BibRef

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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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

Zhang, J.F.[Ji-Fu], Jiang, Y.Y.[Yi-Yong], Chang, K.H.[Kai H.], Zhang, S.[Sulan], Cai, J.H.[Jiang-Hui], Hu, L.H.[Li-Hua],
A concept lattice based outlier mining method in low-dimensional subspaces,
PRL(30), No. 15, 1 November 2009, pp. 1434-1439.
Elsevier DOI 0910
Outliers; Concept lattice; Sparsity coefficient; Density coefficient; Intent reduction 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.[Adriăo Dória], Martins, A.[Allan],
Representative cross information potential clustering,
PRL(34), No. 16, 2013, pp. 2181-2191.
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,
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

Tuzel, O.[Oncel], Porikli, F.M.[Fatih M.], Meer, P.[Peter],
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],
Improving constrained clustering with active query selection,
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,
IJCV(110), No. 1, December 2014, pp. 308-327.
WWW Link. 1411
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Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
Efficient Optimization for Low-Rank Integrated Bilinear Classifiers,
ECCV12(II: 474-487).
Springer DOI 1210
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Kobayashi, T.[Takumi], Yoshikawa, F.[Fumito], Otsu, N.[Nobuyuki],
Cone-restricted kernel subspace methods,
ICIP10(3853-3856).
IEEE DOI 1009
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IEEE DOI 0812
non-negative feature values BibRef

Kobayashi, T.[Takumi], Otsu, N.[Nobuyuki],
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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,
ICPR10(3882-3885).
IEEE DOI 1008
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Earlier:
Efficient reduction of support vectors in kernel-based methods,
ICIP09(2077-2080).
IEEE DOI 0911
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Kobayashi, T.[Takumi],
Discriminative local binary pattern,
MVA(27), No. 8, November 2016, pp. 1175-1186.
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],
BFO Meets HOG: Feature Extraction Based on Histograms of Oriented p.d.f. Gradients for Image Classification,
CVPR13(747-754)
IEEE DOI 1309
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Earlier:
Generalized Mutual Subspace Based Methods for Image Set Classification,
ACCV12(I:578-592).
Springer DOI 1304
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And:
Higher-order Co-occurrence Features based on Discriminative Co-clusters for Image Classification,
BMVC12(64).
DOI Link 1301
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Leyva, E.[Enrique], González, A.[Antonio], Pérez, R.[Raúl],
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Elsevier DOI 1502
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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Elsevier DOI 1506
Intuitionistic fuzzy set BibRef

Bhargavi, M.S., Gowda, S.D.[Sahana D.],
A novel validity index with dynamic cut-off for determining true clusters,
PR(48), No. 11, 2015, pp. 3673-3687.
Elsevier DOI 1506
Clustering BibRef

Hao, H.[Hua], Wang, Q.[Qilong], Li, P.H.[Pei-Hua], Zhang, L.[Lei],
Evaluation of ground distances and features in EMD-based GMM matching for texture classification,
PR(57), No. 1, 2016, pp. 152-163.
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,
PRL(80), No. 1, 2016, pp. 224-230.
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.],
Restricted set classification: Who is there?,
PR(63), No. 1, 2017, pp. 158-170.
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)
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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,
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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],
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PRL(97), No. 1, 2017, pp. 61-68.
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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Mao, Q.[Qi], Wang, L.[Li], Tsang, I.W.[Ivor W.], Sun, Y.[Yijun],
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

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


Kolouri, S.[Soheil], Zou, Y.[Yang], Rohde, G.K.[Gustavo K.],
Sliced Wasserstein Kernels for Probability Distributions,
CVPR16(5258-5267)
IEEE DOI 1612
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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
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Nielsen, F.[Frank], Muzellec, B.[Boris], Nock, R.[Richard],
Classification with mixtures of curved Mahalanobis metrics,
ICIP16(241-245)
IEEE DOI 1610
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An, S.J.[Sen-Jian], Hayat, M.[Munawar], Khan, S.H.[Salman H.], Bennamoun, M.[Mohammed], Boussaid, F.[Farid], Sohel, F.[Ferdous],
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).
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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

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Trading off Distance Metrics vs Accuracy in Incremental Learning Algorithms,
CIARP16(530-538).
Springer DOI 1703
BibRef
Earlier:
On the Impact of Distance Metrics in Instance-Based Learning Algorithms,
IbPRIA15(48-56).
Springer DOI 1506
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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

Strand, R.[Robin], Malmberg, F.[Filip], Saha, P.K.[Punam K.], Linnér, E.[Elisabeth],
The Minimum Barrier Distance: Stability to Seed Point Position,
DGCI14(111-121).
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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, Computer vision, 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

Banerjee, B.[Biplab], Mishra, P.K.[Pradeep Kumar], Varma, S.[Surender], Mohan, B.K.[Buddhiraju Krishna],
A Novel Graph Based Clustering Technique for Hybrid Segmentation of Multi-spectral Remotely Sensed Images,
ACIVS13(274-285).
Springer DOI 1311
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.[Liwei],
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
BibRef

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
BibRef

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 See also second-order analysis of stationary point processes, The. BibRef

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
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Rota Bulo, S.[Samuel], Pelillo, M.[Marcello],
Probabilistic Clustering Using the Baum-Eagon Inequality,
ICPR10(1429-1432).
IEEE DOI 1008
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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).
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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
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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).
Springer DOI 0708
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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
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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.[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
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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],
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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
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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
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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
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Yang, M.H.[Ming-Hsuan],
Extended Isomap for classification,
ICPR02(III: 615-618).
IEEE DOI 0211
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Muller, N., Herbst, B.M.,
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ICPR02(III: 883-886).
IEEE DOI 0211
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Zhu, Y.[Ying], Schwartz, S.,
Discriminant analysis and adaptive wavelet feature selection for statistical object detection,
ICPR02(IV: 86-89).
IEEE DOI 0211
BibRef

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


Last update:Nov 11, 2017 at 13:31:57