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0204
BibRef
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Prototype selection for finding efficient representations of
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IEEE DOI
0211
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IEEE DOI
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1410
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IEEE DOI
1006
Eigenvalues and eigenfunctions
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0512
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Springer DOI
0608
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Spillmann, B.[Barbara],
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Springer DOI
0608
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Harol, A.[Artsiom],
Lee, W.J.[Wan-Jui],
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0812
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IEEE DOI
0609
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HTML Version.
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IEEE DOI
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0308
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Earlier:
Clustering Under a Hypothesis of Smooth Dissimilarity Increments,
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IEEE DOI
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1206
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Earlier:
On the Distribution of Dissimilarity Increments,
IbPRIA11(192-199).
Springer DOI
1106
Dissimilarity increments; Partitional clustering; Likelihood-ratio
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Aidos, H.[Helena],
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Springer DOI
1307
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Aidos, H.[Helena],
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0506
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IEEE DOI
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IEEE DOI
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Elsevier DOI
0401
The data are first transformed so that the pattern vector components
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Hoti, F.,
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IEEE DOI
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Symbolic data analysis; Clustering analysis; Mixed feature-type
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Symbolic Data Analysis; Clustering algorithms; Normal symbolic form;
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Symbolic data analysis; Partitional clustering methods; Symbolic
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Clustering analysis
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Partitioning clustering algorithms
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0601
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Jiao, L.C.[Li-Cheng],
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Kernel matching pursuit classifier ensemble,
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0604
Kernel Matching Pursuit Classifier; Ensemble Method; KMPC ensemble;
Pattern recognition
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0606
Feature extraction; Mutual information; Optimal subspace projection
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Hild, II, K.E.[Kenneth E.],
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Torkkola, K.[Kari],
Principe, J.C.[Jose C.],
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PAMI(28), No. 9, September 2006, pp. 1385-1392.
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0608
Train feature extraction independently of the classification.
Maximize mutual information between the labels and the output
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0611
BibRef
Earlier:
Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic,
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Springer DOI
0601
Graph theoretic cut; Information theory; Parzen window density estimation;
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0606
Complex data; Modular clustering; Feature weighting;
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Wania, A.,
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0711
Complex data; Modular clustering; Feature weighting; Cooperative coevolution
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0804
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1011
Sequence analysis; Time series clustering; Dynamic time warping;
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Neighborhood Topologies in Fully Informed and Best-of-Neighborhood
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0606
Discover optimal regions by emulating neighbors.
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On the Impact of Dissimilarity Measure in k-Modes Clustering Algorithm,
PAMI(29), No. 3, March 2007, pp. 503-507.
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0702
See also Alternative Extension of the k-Means Algorithm for Clustering Categorical Data, An.
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Linear manifold clustering in high dimensional spaces by stochastic
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0707
Clustering; Linear manifold; Subspace; Histogram thresholding;
Data exploration; Random projections.
Cluster center is not a single point, for dispersed centers.
BibRef
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Hayashi, A.[Akira],
A Redundancy-Based Measure of Dissimilarity among Probability
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PAMI(30), No. 1, January 2008, pp. 76-88.
IEEE DOI
0711
Measure difference between clusters.
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Jung, G.J.,
Oh, Y.H.,
Information Distance-Based Subvector Clustering for ASR Parameter
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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.
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0802
BibRef
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A density-based cluster validity approach using multi-representatives,
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0803
Cluster validity; Clustering; Quality assessment; Unsupervised learning
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Eng, H.L.[How-Lung],
Between Classification-Error Approximation and Weighted Least-Squares
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0803
BibRef
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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
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Irpino, A.[Antonio],
Verde, R.[Rosanna],
Dynamic clustering of interval data using a Wasserstein-based distance,
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0804
Interval data; Clustering; Wasserstein distance; Inertia
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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
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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
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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
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Ortiz-de-Lazcano-Lobato, J.M.[Juan Miguel],
Soft clustering for nonparametric probability density function
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Elsevier DOI
0811
Probability density estimation; Nonparametric modeling; Soft
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Lopez-Rubio, E.,
A Histogram Transform for Probability Density Function Estimation,
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1404
Estimation
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Time Warp Edit Distance with Stiffness Adjustment for Time Series
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IEEE DOI
0901
BibRef
Lazebnik, S.[Svetlana],
Raginsky, M.[Maxim],
Supervised Learning of Quantizer Codebooks by Information Loss
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PAMI(31), No. 7, July 2009, pp. 1294-1309.
IEEE DOI
0905
BibRef
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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
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Feng, L.[Liang],
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Wang, Y.X.[Yu-Xuan],
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1008
Hierarchical clustering; Divisive clustering; Particle swarm optimizer
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1008
Applied to the classification of altimetric waveforms backscattered
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Yang, Y.,
Xu, D.,
Nie, F.,
Yan, S.,
Zhuang, Y.,
Image Clustering Using Local Discriminant Models and Global Integration,
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1003
clustering using local discriminant models and global integration.
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1101
Manifold learning; Tangent space; Dynamical neighborhood; Sampling
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1101
BibRef
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1508
Analytical models
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1107
Distance based clustering; Arbitrary shaped clusters; Leaders;
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Clustering by Sorting Potential Values (CSPV):
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1206
Clustering; Potential field; Spatial distribution; Distance matrix;
Pattern recognition
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Lu, Y.G.[Yong-Gang],
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1302
Clustering; Algorithm; Pattern recognition; Potential field
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Wei, X.[Xin],
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The infinite Student's t-factor mixture analyzer for robust clustering
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1208
Infinite Student's t-factor mixture analyzer; Nonparametric Bayesian
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In search of optimal centroids on data clustering using a binary search
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A binary search algorithm; Optimal centroids; Data clustering
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1302
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Wang, Z.L.[Zi-Lei],
Feng, J.S.[Jia-Shi],
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1310
clutter
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Wang, Z.L.[Zi-Lei],
Feng, J.S.[Jia-Shi],
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Collaborative Linear Coding for Robust Image Classification,
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1509
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Mohammadi, A.,
Asif, A.,
Decentralized Conditional Posterior Cramér-Rao Lower Bound for
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1302
BibRef
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Ding, W.[Wei],
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1304
Local learning; Distance metrics learning
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Mai, H.T.[Hai Thanh],
Kim, J.[Jaeho],
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Springer DOI
1304
optimizing queries. Find cluster centers, expand them out.
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Bai, L.[Liang],
Liang, J.[Jiye],
Dang, C.Y.[Chuang-Yin],
Cao, F.Y.[Fu-Yuan],
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1305
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Bhattacherjee, V.[Vandana],
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DOI Link
1309
BibRef
Araújo, D.[Daniel],
Neto, A.D.[Adriăo Dória],
Martins, A.[Allan],
Representative cross information potential clustering,
PRL(34), No. 16, 2013, pp. 2181-2191.
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1310
Clustering. Interactions between distributions.
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de Muinck, E.[Ebo],
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PRL(34), No. 16, 2013, pp. 2192-2198.
Elsevier DOI
1310
Spatial statistics
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Elsevier DOI
1402
Clustering
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Anand, S.,
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Semi-Supervised Kernel Mean Shift Clustering,
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IEEE DOI
1406
Clustering algorithms
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Porikli, F.M.[Fatih M.],
Meer, P.[Peter],
Kernel methods for weakly supervised mean shift clustering,
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IEEE DOI
0909
BibRef
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
BibRef
Earlier:
An Efficient Active Constraint Selection Algorithm for Clustering,
ICPR10(2969-2972).
IEEE DOI
1008
Active semi-supervised clustering
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Kobayashi, T.[Takumi],
Low-Rank Bilinear Classification:
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BibRef
Kobayashi, T.[Takumi],
Otsu, N.[Nobuyuki],
Efficient Optimization for Low-Rank Integrated Bilinear Classifiers,
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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
BibRef
Earlier: A1, A3, Only:
Cone-restricted subspace methods,
ICPR08(1-4).
IEEE DOI
0812
non-negative feature values
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Kobayashi, T.[Takumi],
Otsu, N.[Nobuyuki],
Von Mises-Fisher Mean Shift for Clustering on a Hypersphere,
ICPR10(2130-2133).
IEEE DOI
1008
BibRef
Kobayashi, T.[Takumi],
Otsu, N.[Nobuyuki],
Bag of Hierarchical Co-occurrence Features for Image Classification,
ICPR10(3882-3885).
IEEE DOI
1008
BibRef
Earlier:
Efficient reduction of support vectors in kernel-based methods,
ICIP09(2077-2080).
IEEE DOI
0911
BibRef
Kobayashi, T.[Takumi],
Discriminative local binary pattern,
MVA(27), No. 8, November 2016, pp. 1175-1186.
Springer DOI
1612
BibRef
Earlier:
Structured Feature Similarity with Explicit Feature Map,
CVPR16(1211-1219)
IEEE DOI
1612
BibRef
Earlier:
Discriminative Local Binary Pattern for Image Feature Extraction,
CAIP15(I:594-605).
Springer DOI
1511
BibRef
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
BibRef
Kobayashi, T.[Takumi],
Learning Additive Kernel For Feature Transformation and Its Application
to CNN Features,
BMVC16(xx-yy).
HTML Version.
1805
BibRef
Leyva, E.[Enrique],
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Three new instance selection methods based on local sets:
A comparative study with several approaches from a bi-objective
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PR(48), No. 4, 2015, pp. 1523-1537.
Elsevier DOI
1502
Local sets
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Cleuziou, G.[Guillaume],
Moreno, J.G.[Jose G.],
Kernel methods for point symmetry-based clustering,
PR(48), No. 9, 2015, pp. 2812-2830.
Elsevier DOI
1506
Pattern recognition. Clusters with point symmetric shape.
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Du, W.S.[Wen Sheng],
Hu, B.Q.[Bao Qing],
Aggregation distance measure and its induced similarity measure
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PRL(60-61), No. 1, 2015, pp. 65-71.
Elsevier DOI
1506
Intuitionistic fuzzy set
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Bhargavi, M.S.,
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A novel validity index with dynamic cut-off for determining true
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PR(48), No. 11, 2015, pp. 3673-3687.
Elsevier DOI
1506
Clustering
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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
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PR(57), No. 1, 2016, pp. 152-163.
Elsevier DOI
1605
Earth Mover's Distance. Gaussian mixture models. Texture classification
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Duong, T.[Tarn],
Beck, G.[Gaël],
Azzag, H.[Hanene],
Lebbah, M.[Mustapha],
Nearest neighbour estimators of density derivatives, with application
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PRL(80), No. 1, 2016, pp. 224-230.
Elsevier DOI
1609
Gradient ascent
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Lu, N.[Na],
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Clustering Tree-Structured Data on Manifold,
PAMI(38), No. 10, October 2016, pp. 1956-1968.
IEEE DOI
1609
Algorithm design and analysis
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Chen, M.[Mei],
Li, L.J.[Long-Jie],
Wang, B.[Bo],
Cheng, J.J.[Jian-Jun],
Pan, L.[Lina],
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Elsevier DOI
1609
Clustering algorithm
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Tan, P.[Pan],
Zhou, Z.C.[Zheng-Chun],
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A Construction of Codebooks Nearly Achieving the Levenstein Bound,
SPLetters(23), No. 10, October 2016, pp. 1306-1309.
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1610
product codes
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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
BibRef
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
BibRef
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
BibRef
Earlier: A1, A3, A4, Only:
FG15(1-7)
IEEE DOI
1508
face recognition
BibRef
Yang, M.[Meng],
Zhu, P.F.[Peng-Fei],
Van Gool, L.J.,
Zhang, L.[Lei],
Face Recognition Based on Regularized Nearest Points Between Image
Sets,
FG13(1-7)
IEEE DOI
1309
face recognition. Cluster distances.
BibRef
Wang, Y.,
Zhang, L.,
Deng, H.,
Lu, J.,
Huang, H.,
Zhang, L.,
Liu, J.,
Tang, H.,
Xing, X.,
Learning a Discriminative Distance Metric With Label Consistency for
Scene Classification,
GeoRS(55), No. 8, August 2017, pp. 4427-4440.
IEEE DOI
1708
Encoding, Feature extraction, Learning systems, Measurement,
Optimization, Remote sensing, Spatial resolution,
Distance metric learning (DML), high spatial resolution (HSR),
label consistency (LC), optimization, scene, classification
BibRef
Datta, S.[Shounak],
Mullick, S.S.[Sankha Subhra],
Das, S.[Swagatam],
Generalized mean based back-propagation of errors for ambiguity
resolution,
PRL(94), No. 1, 2017, pp. 22-29.
Elsevier DOI
1708
Ambiguity resolution. Datapoints have multiple labels.
BibRef
Ortakaya, A.F.[Ahmet Fatih],
Independently weighted value difference metric,
PRL(97), No. 1, 2017, pp. 61-68.
Elsevier DOI
1709
Categorical classification
BibRef
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
BibRef
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.[Tomá],
Kolingerová, I.[Ivana],
Potucková, M.[Markéta],
Cábelka, M.[Miroslav],
tefanová, 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
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.,
Traboulsi, Y.E.,
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
BibRef
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
BibRef
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).
HTML Version.
1410
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).
WWW Link.
1302
BibRef
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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Earlier:
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Emergent09(2095-2100).
IEEE DOI
0910
Apply mode filter to deal with errors in training data.
BibRef
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ICPR10(3898-3901).
IEEE DOI
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ICPR10(1429-1432).
IEEE DOI
1008
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ICPR10(4052-4055).
IEEE DOI
1008
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IASP10(243-247).
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ISVC09(II: 747-756).
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0911
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ICDAR09(471-475).
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0907
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0610
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On Subspace Distance,
ICIAR06(II: 81-89).
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0610
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Alternative Approaches and Algorithms for Classification,
ICIAR06(II: 35-46).
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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.
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Chen, J.[Jie],
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0812
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ICPR08(1-4).
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0812
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Earlier:
Grouping with Asymmetric Affinities: A Game-Theoretic Perspective,
CVPR06(I: 292-299).
IEEE DOI
0606
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0806
BibRef
Earlier: A2, A1, A3:
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CVPR06(I: 103-108).
IEEE DOI
0606
Each member class is represented by a conic section, classification by
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0211
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See also On the Use of SDF-Type Filters for Distortion Parameter Estimation.
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The performance analysis of a chi-square similarity measure for topic
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ICPR02(IV: 285-288).
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0211
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Ujiie, H.,
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0211
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ICIP97(II: 430-433).
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9006
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Mixture Models, Mixed Pixels .