14.1.3.5 Number of Features, Dimensionality Reduction

Chapter Contents (Back)
Dimensionality. Dimensionality Reduction. See also Intrinsic Dimensionality. See also Computation and Analysis of Principal Components, Eigen Values, SVD. See also Hyperspectral Data, Dimensionality Reduction, Band Selection. See also Graph Embedding Clustering.

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Zhao, D.F.[Dong-Fang], Yang, L.[Li],
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Yang, L.[Li],
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And:
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Yang, L.[Li],
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Elsevier DOI 0411
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Sun, Q.S.[Quan-Sen], Liu, Z.D.[Zheng-Dong], Heng, P.A.[Pheng-Ann], Xia, D.S.[De-Sen],
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PR(38), No. 3, March 2005, pp. 449-452.
Elsevier DOI 0412
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Donoho, D.L.[David L.], Grimes, C.[Carrie],
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JMIV(23), No. 1, July 2005, pp. 5-24.
Springer DOI 0505
Analysis of ISOMap classification. ( See also Global Geometric Framework for Nonlinear Dimensionality Reduction, A. ) BibRef

Kouropteva, O.[Olga], Okun, O.[Oleg], Pietikäinen, M.[Matti],
Incremental locally linear embedding,
PR(38), No. 10, October 2005, pp. 1764-1767.
Elsevier DOI 0508
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Earlier:
Incremental Locally Linear Embedding Algorithm,
SCIA05(521-530).
Springer DOI 0506
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Hadid, A., Kouropteva, O., Pietikanen, M.,
Unsupervised Learning Using Locally Linear Embedding: Experiments with Face Pose Analysis,
ICPR02(I: 111-114).
IEEE DOI 0211
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Benito, M.[Monica], Pena, D.[Daniel],
A fast approach for dimensionality reduction with image data,
PR(38), No. 12, December 2005, pp. 2400-2408.
Elsevier DOI 0510
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Zhang, K., Chan, L.W.,
Dimension Reduction as a Deflation Method in ICA,
SPLetters(13), No. 1, January 2006, pp. 45-48.
IEEE DOI 0601
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Hsieh, P.F.[Pi-Fuei], Wang, D.S.[Deng-Shiang], Hsu, C.W.[Chia-Wei],
A Linear Feature Extraction for Multiclass Classification Problems Based on Class Mean and Covariance Discriminant Information,
PAMI(28), No. 2, February 2006, pp. 223-235.
IEEE DOI 0601
Use pariwise accuracy criterion rather than LDA for dimensionality reduction. BibRef

Law, M.H.C.[Martin H.C.], Jain, A.K.[Anil K.],
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PAMI(28), No. 3, March 2006, pp. 377-391.
IEEE DOI 0602
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Hu, Q.H.[Qing-Hua], Yu, D.R.[Da-Ren], Xie, Z.X.[Zong-Xia],
Information-preserving hybrid data reduction based on fuzzy-rough techniques,
PRL(27), No. 5, 1 April 2006, pp. 414-423.
Elsevier DOI 0604
Attribute reduction; Hybrid data; Fuzzy-rough set; Information measure BibRef

Hu, Q.H.[Qing-Hua], Xie, Z.X.[Zong-Xia], Yu, D.R.[Da-Ren],
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PR(40), No. 12, December 2007, pp. 3509-3521.
Elsevier DOI 0709
Numerical feature; Categorical feature; Feature selection; Attribute reduction; Fuzzy set; Rough set; Inclusion degree BibRef

Zhao, D.L.[De-Li],
Formulating LLE using alignment technique,
PR(39), No. 11, November 2006, pp. 2233-2235.
Elsevier DOI 0608
LLE; LTSA; Nonlinear dimensionality reduction; Manifold learning BibRef

Lafon, S.[Stephane], Lee, A.B.[Ann B.],
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameterization,
PAMI(28), No. 9, September 2006, pp. 1393-1403.
IEEE DOI 0608
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Wei, H.L.[Hua-Liang], Billings, S.A.,
Feature Subset Selection and Ranking for Data Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 162-166.
IEEE DOI 0701
Forward Orthogonal Search. Select features 1 at a time. BibRef

Yu, J., Tian, Q., Rui, T., Huang, T.S.,
Integrating Discriminant and Descriptive Information for Dimension Reduction and Classification,
CirSysVideo(17), No. 3, March 2007, pp. 372-377.
IEEE DOI 0703
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Kokiopoulou, E.[Effrosyni], Saad, Y.[Yousef],
Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique,
PAMI(29), No. 12, December 2007, pp. 2143-2156.
IEEE DOI 0711
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Kokiopoulou, E.[Effrosyni], Saad, Y.[Yousef],
Enhanced graph-based dimensionality reduction with repulsion Laplaceans,
PR(42), No. 11, November 2009, pp. 2392-2402.
Elsevier DOI 0907
Linear dimensionality reduction; Orthogonal projections; Supervised learning; Face recognition; Graph Laplacean BibRef

Fu, Y.[Yun], Huang, T.S.[Thomas S.],
Image Classification Using Correlation Tensor Analysis,
IP(17), No. 2, February 2008, pp. 226-234.
IEEE DOI 0801
Correlation-based similarity metric in supervised multilinear discriminant subspace learning can improve classification performance. BibRef

Fu, Y.[Yun], Yan, S.C.[Shui-Cheng], Huang, T.S.[Thomas S.],
Correlation Metric for Generalized Feature Extraction,
PAMI(30), No. 12, December 2008, pp. 2229-2235.
IEEE DOI 0811
Alternative to PCA BibRef

Yang, J., Yan, S.C.[Shui-Cheng], Huang, T.S.[Thomas S.],
Ubiquitously Supervised Subspace Learning,
IP(18), No. 2, February 2009, pp. 241-249.
IEEE DOI 0901
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Fu, Y.[Yun], Liu, M.[Ming], Huang, T.S.[Thomas S.],
Conformal Embedding Analysis with Local Graph Modeling on the Unit Hypersphere,
ComponentAnalysis07(1-6).
IEEE DOI 0706
project high dimensional data on unit sphere, maintain neighbor relations. BibRef

Sanguinetti, G.[Guido],
Dimensionality Reduction of Clustered Data Sets,
PAMI(30), No. 3, March 2008, pp. 535-540.
IEEE DOI 0801
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Xue, H.[Hui], Chen, S.C.[Song-Can], Zeng, X.Q.[Xiao-Qin],
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PR(41), No. 5, May 2008, pp. 1496-1507.
Elsevier DOI 0711
Localized generalization error model; Stochastic sensitivity measure; Locality regularization (LR); Classifier Learning; Pattern classification BibRef

Lin, T.[Tong], Zha, H.B.[Hong-Bin],
Riemannian Manifold Learning,
PAMI(30), No. 5, May 2008, pp. 796-809.
IEEE DOI 0803
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Lin, T.[Tong], Zha, H.B.[Hong-Bin], Lee, S.U.[Sang Uk],
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ECCV06(I: 44-55).
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Guo, Y.[Yi], Gao, J.B.[Jun-Bin], Kwan, P.W.[Paul W.],
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PAMI(30), No. 8, August 2008, pp. 1490-1495.
IEEE DOI 0806
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Rueda, L.G.[Luis G.], Herrera, M.[Myriam],
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PR(41), No. 10, October 2008, pp. 3138-3152.
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Earlier:
A New Approach to Multi-class Linear Dimensionality Reduction,
CIARP06(634-643).
Springer DOI 0611
BibRef
And:
A Theoretical Comparison of Two Linear Dimensionality Reduction Techniques,
CIARP06(624-633).
Springer DOI 0611
Linear dimensionality reduction; Pattern classification; Discriminant analysis See also On Optimal Pairwise Linear Classifiers for Normal Distributions: The D-Dimensional Case. BibRef

Rueda, L.G.[Luis G.], Herrera, M.[Myriam],
A theoretical comparison of two-class Fisher's and heteroscedastic linear dimensionality reduction schemes,
PRL(29), No. 16, 1 December 2008, pp. 2092-2098.
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Linear dimensionality reduction; Heteroscedastic classifiers; Classification error BibRef

Rueda, L.G.[Luis G.], Oommen, B.J.[B. John], Henriquez, C.[Claudio],
Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes,
PR(43), No. 7, July 2010, pp. 2456-2465.
Elsevier DOI 1003
BibRef
Earlier: A1, A3, A2:
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CIARP08(301-308).
Springer DOI 0809
Linear dimensionality reduction; Fisher's discriminant analysis; Heteroscedastic discriminant analysis; Chernoff-based dimensionality reduction; Pairwise multi-class classification BibRef

Shen, C.H.[Chun-Hua], Li, H.D.[Hong-Dong], Brooks, M.J.[Michael J.],
Supervised dimensionality reduction via sequential semidefinite programming,
PR(41), No. 12, December 2008, pp. 3644-3652.
Elsevier DOI 0810
Dimensionality reduction; Semidefinite programming; Linear discriminant analysis Zip codes, faces. BibRef

Shen, C.H.[Chun-Hua], Kim, J.[Junae], Wang, L.[Lei],
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CVPR11(2601-2608).
IEEE DOI 1106
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Scoleri, T., Chojnacki, W., Brooks, M.J.[Michael J.],
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IET-CV(2), No. 4, December 2008, pp. 218-227.
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Scoleri, T.,
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IEEE DOI 0812
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Nie, F.P.[Fei-Ping], Xiang, S.M.[Shi-Ming], Song, Y.Q.[Yang-Qiu], Zhang, C.S.[Chang-Shui],
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PR(42), No. 1, January 2009, pp. 105-114.
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Nie, F.P.[Fei-Ping], Xiang, S.M.[Shi-Ming], Jia, Y.Q.[Yang-Qing], Zhang, C.S.[Chang-Shui],
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PR(42), No. 11, November 2009, pp. 2615-2627.
Elsevier DOI 0907
Subspace learning; Discriminant analysis; Dimensionality reduction; Trace ratio; Semi-supervised learning BibRef

Nie, F.P.[Fei-Ping], Xu, D., Li, X., Xiang, S.M.[Shi-Ming],
Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression,
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IEEE DOI 1106
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Hou, C., Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.,
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SPLetters(16), No. 4, April 2009, pp. 303-306.
IEEE DOI 0903
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Hou, C., Nie, F.P., Yi, D., Wu, Y.,
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IEEE DOI 1301
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Liu, Y.[Yang], Liu, Y.[Yan], Chan, K.C.C.[Keith C.C.],
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PR(42), No. 2, February 2009, pp. 229-242.
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Dimensionality reduction; Rushes editing; Manifold learning; Isometric feature mapping; Multi-layer Isometric feature mapping BibRef

Liu, Y.[Yang], Liu, Y.[Yan], Chan, K.C.C.[Keith C.C.],
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CVIU(115), No. 3, March 2011, pp. 300-309.
Elsevier DOI 1103
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Earlier:
Multilinear Isometric Embedding for visual pattern analysis,
Subspace09(212-218).
IEEE DOI 0910
Image and video classification; Local-based method; Maximum margin classifier; Tensor representation BibRef

Xu, D.[Dong], Yan, S.C.[Shui-Cheng], Lin, S.[Stephen], Huang, T.S.[Thomas S.],
Convergent 2-D Subspace Learning With Null Space Analysis,
CirSysVideo(18), No. 12, December 2008, pp. 1753-1759.
IEEE DOI 0812
See also Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis. BibRef

Xu, D.[Dong], Yan, S.C.[Shui-Cheng], Lin, S.[Stephen], Huang, T.S.[Thomas S.], Chang, S.F.[Shih-Fu],
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PAMI(31), No. 10, October 2009, pp. 1913-1920.
IEEE DOI 0909
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Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Lin, S.[Stephen], Huang, T.S.[Thomas S.], Chang, S.F.[Shih-Fu],
Element Rearrangement for Tensor-Based Subspace Learning,
CVPR07(1-8).
IEEE DOI 0706
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Xu, D., Yan, X.,
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IP(18), No. 7, July 2009, pp. 1671-1676.
IEEE DOI 0906
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Pang, Y., Yuan, Y., Li, X.L.,
Effective Feature Extraction in High-Dimensional Space,
SMC-B(38), No. 6, December 2008, pp. 1652-1656.
IEEE DOI 0812
BibRef

Pang, Y., Yuan, Y., Li, X.L.,
Iterative Subspace Analysis Based on Feature Line Distance,
IP(18), No. 4, April 2009, pp. 903-907.
IEEE DOI 0903
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Pan, Y.Z.[Yao-Zhang], Ge, S.Z.S.[Shu-Zhi Sam], Al Mamun, A.[Abdullah],
Weighted locally linear embedding for dimension reduction,
PR(42), No. 5, May 2009, pp. 798-811.
Elsevier DOI 0902
Nonlinear dimensionality reduction; Manifold learning; Feature extraction; Locally linear embedding BibRef

Ge, S.Z.S.[Shu-Zhi Sam], Guan, F.[Feng], Pan, Y.Z.[Yao-Zhang], Loh, A.P.[Ai Poh],
Neighborhood linear embedding for intrinsic structure discovery,
MVA(21), No. 3, April 2010, pp. xx-yy.
Springer DOI 1003
Learning to discover neighborhood relationships. BibRef

Ge, S.Z.S.[Shu-Zhi Sam], He, H.S.[Hong-Sheng], Shen, C.Y.[Cheng-Yao],
Geometrically local embedding in manifolds for dimension reduction,
PR(45), No. 4, April 2012, pp. 1455-1470.
Elsevier DOI 1112
Geometry distance; Dimension reduction; Linear manifolds; GLE BibRef

Yan, S.C.[Shui-Cheng], Wang, H.[Huan], Tu, J., Tang, X.[Xiaoou], Huang, T.S.[Thomas S.],
Mode-kn Factor Analysis for Image Ensembles,
IP(18), No. 3, March 2009, pp. 670-676.
IEEE DOI 0903
BibRef

Wang, H.[Huan], Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Tang, X.[Xiaoou], Huang, T.S.[Thomas S.],
Trace Ratio vs. Ratio Trace for Dimensionality Reduction,
CVPR07(1-8).
IEEE DOI 0706
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Renard, N., Bourennane, S.,
Dimensionality Reduction Based on Tensor Modeling for Classification Methods,
GeoRS(47), No. 4, April 2009, pp. 1123-1131.
IEEE DOI 0903
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Kim, J.K.[Jong Kyoung], Choi, S.J.[Seung-Jin],
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PR(42), No. 9, September 2009, pp. 2020-2028.
Elsevier DOI 0905
b-Matching; Cluster utility; Graph-based clustering; Regular graphs BibRef

Lee, S.H.[Seung-Hak], Choi, S.J.[Seung-Jin],
Landmark MDS ensemble,
PR(42), No. 9, September 2009, pp. 2045-2053.
Elsevier DOI 0905
Dimensionality reduction; Embedding; Multidimensional scaling (MDS); Unsupervised learning BibRef

Hou, C.P.[Chen-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.[Yi], Jiao, Y.Y.[Yuan-Yuan],
Stable local dimensionality reduction approaches,
PR(42), No. 9, September 2009, pp. 2054-2066.
Elsevier DOI 0905
Dimensionality reduction; Manifold learning; Locally linear embedding; Laplacian eigenmaps; Local tangent space alignment BibRef

Hou, C.P.[Chen-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.[Yi], Nie, F.P.[Fei-Ping],
Multiple view semi-supervised dimensionality reduction,
PR(43), No. 3, March 2010, pp. 720-730.
Elsevier DOI 1001
Dimensionality reduction; Semi-supervised; Multiple view; Domain knowledge BibRef

Li, J.[Jun], Hao, P.W.[Peng-Wei],
Finding representative landmarks of data on manifolds,
PR(42), No. 11, November 2009, pp. 2335-2352.
Elsevier DOI 0907
Manifold learning; Data representation; Dimensionality reduction BibRef

Gullo, F.[Francesco], Ponti, G.[Giovanni], Tagarelli, A.[Andrea], Greco, S.[Sergio],
A time series representation model for accurate and fast similarity detection,
PR(42), No. 11, November 2009, pp. 2998-3014.
Elsevier DOI 0907
Time series data; Representation models; Similarity detection; Dimensionality reduction; Clustering; Classification BibRef

Hu, X.Q.[Xiao-Qin], Yang, Z.[Zhixia], Jing, L.[Ling],
An incremental dimensionality reduction method on discriminant information for pattern classification,
PRL(30), No. 15, 1 November 2009, pp. 1416-1423.
Elsevier DOI 0910
Dimensionality reduction; Pattern classification; Discriminant mapping BibRef

Zhang, T., Huang, K., Li, X., Yang, J., Tao, D.,
Discriminative Orthogonal Neighborhood-Preserving Projections for Classification,
SMC-B(40), No. 1, February 2010, pp. 253-263.
IEEE DOI 0911
To overcome outlier problems in linear embedded classification. BibRef

Jia, P.[Peng], Yin, J.S.[Jun-Song], Huang, X.S.[Xin-Sheng], Hu, D.[Dewen],
Incremental Laplacian eigenmaps by preserving adjacent information between data points,
PRL(30), No. 16, 1 December 2009, pp. 1457-1463.
Elsevier DOI 0911
Laplacian eigenmaps; Incremental learning; Locally linear construction; Nonlinear dimensionality reduction BibRef

Dianat, R., Kasaei, S.,
Dimension Reduction of Optical Remote Sensing Images via Minimum Change Rate Deviation Method,
GeoRS(48), No. 1, January 2010, pp. 198-206.
IEEE DOI 1001
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Hsieh, P.F.[Pi-Fuei], Chou, P.W.[Po-Wen], Chung, H.Y.[Hsueh-Yi],
An MRF-based kernel method for nonlinear feature extraction,
IVC(28), No. 3, March 2010, pp. 502-517.
Elsevier DOI 1001
Feature extraction; Dimensionality reduction; Kernel trick; Classification BibRef

Wang, J.[Jing], Zhang, Z.Y.[Zhen-Yue],
Nonlinear embedding preserving multiple local-linearities,
PR(43), No. 4, April 2010, pp. 1257-1268.
Elsevier DOI 1002
Manifold learning; Dimensionality reduction; Weight vector; Stability of algorithm BibRef

Zhang, Z.Y.[Zhen-Yue], Wang, J.[Jing], Zha, H.Y.[Hong-Yuan],
Adaptive Manifold Learning,
PAMI(34), No. 2, February 2012, pp. 253-265.
IEEE DOI 1112
Seek low-dimensional parameterization of high-dimensional data. Assume local can approximate global. BibRef

Liang, Z.Z.[Zhi-Zheng], Li, Y.F.[You-Fu],
A regularization framework for robust dimensionality reduction with applications to image reconstruction and feature extraction,
PR(43), No. 4, April 2010, pp. 1269-1281.
Elsevier DOI 1002
Regularization framework; Nonlinear eigenvalue problem; SCF iteration; Robust; Feature extraction; Image reconstruction BibRef

Chu, D.L.[De-Lin], Thye, G.S.[Goh Siong],
A new and fast implementation for null space based linear discriminant analysis,
PR(43), No. 4, April 2010, pp. 1373-1379.
Elsevier DOI 1002
Dimensionality reduction; Linear discriminant analysis; Null space based linear discriminant analysis; QR factorization; Singular value decomposition BibRef

Czarnowski, I.[Ireneusz],
Prototype selection algorithms for distributed learning,
PR(43), No. 6, June 2010, pp. 2292-2300.
Elsevier DOI 1003
Distributed data mining; Distributed learning; Data reduction; Instance selection BibRef

Lin, B.B.[Bin-Bin], He, X.F.[Xiao-Fei], Zhou, Y.[Yuan], Liu, L.G.[Li-Gang], Lu, K.[Ke],
Approximately harmonic projection: Theoretical analysis and an algorithm,
PR(43), No. 10, October 2010, pp. 3307-3313.
Elsevier DOI 1007
Manifold learning; Dimensionality reduction; Linear projection; Harmonic function BibRef

Qu, H.N.[Hai-Ni], Li, G.Z.[Guo-Zheng], Xu, W.S.[Wei-Sheng],
An asymmetric classifier based on partial least squares,
PR(43), No. 10, October 2010, pp. 3448-3457.
Elsevier DOI 1007
Partial least squares; Dimension reduction; Classification; Unbalanced data BibRef

Nie, F.P.[Fei-Ping], Xu, D.[Dong], Tsang, I.W.H., Zhang, C.S.[Chang-Shui],
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction,
IP(19), No. 7, July 2010, pp. 1921-1932.
IEEE DOI 1007
BibRef

Yan, S.C.[Shui-Cheng], Hu, Y.X.[Yu-Xiao], Xu, D.[Dong], Zhang, H.J.[Hong-Jiang], Zhang, B.Y.[Ben-Yu], Cheng, Q.S.[Qian-Sheng],
Nonlinear Discriminant Analysis on Embedded Manifold,
CirSysVideo(17), No. 4, April 2007, pp. 468-477.
IEEE DOI 0705
Discriminant analysis problem. New cluster approach to get balanced clusters. BibRef

Lewandowski, M.[Michal], Makris, D.[Dimitrios], Nebel, J.C.[Jean-Christophe],
Automatic configuration of spectral dimensionality reduction methods,
PRL(31), No. 12, 1 September 2010, pp. 1720-1727.
Elsevier DOI 1008
Dimensionality reduction; Locally Linear Embedding; Isomap; Laplacian Eigenmaps; Mutual information; Radial Basis Function network BibRef

Lee, J.A.[John A.], Verleysen, M.[Michel],
Scale-independent quality criteria for dimensionality reduction,
PRL(31), No. 14, 15 October 2010, pp. 2248-2257.
Elsevier DOI 1003
Dimensionality reduction; Embedding; Manifold learning; Quality assessment BibRef

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Elsevier DOI 1003
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Elsevier DOI 1011
Distance concentration; Dimensionality reduction; Feature selection; Projection pursuit; Sure independence screening BibRef

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Feature selection; Dimensionality reduction; Classification techniques; Case-Based Reasoning; Rough Set Theory BibRef

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Dimensionality reduction; Pattern recognition; Nearest-neighbor classifier BibRef

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Dimensionality reduction; Manifold learning; Nystrom approximation; Isomap; Ensemble learning; High dimensional affine transformation BibRef

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Manifold learning; Incremental learning; Dimensionality reduction; Spectral embedding methods; Hessian eigenmaps BibRef

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IEEE DOI 1109
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Gaussian Processes; Marginalized variational inference; Bayesian models BibRef

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Faivishevsky, L.[Lev], Goldberger, J.[Jacob],
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Unsupervised dimensionality reduction; Mutual information; Clustering BibRef

Zhu, X.F.[Xiao-Feng], Huang, Z.[Zi], Shen, H.T.[Heng Tao], Cheng, J.[Jian], Xu, C.S.[Chang-Sheng],
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Dimensionality reduction; Mixed kernel; Canonical Correlation Analysis; Model selection BibRef

Gu, N.[Nannan], Fan, M.Y.[Ming-Yu], Qiao, H.[Hong], Zhang, B.[Bo],
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IEEE DOI 1206
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Zhang, Z.[Zhao], Zhao, M.[Mingbo], Chow, T.W.S.[Tommy W.S.],
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Elsevier DOI 1208
Dimensionality reduction; Large margin projection; Manifold visualization; Pairwise constraints; Locality preservation; Multimodality preservation; Kernel method; Pattern classification BibRef

Zhao, M.[Mingbo], Zhang, Z.[Zhao], Chow, T.W.S.[Tommy W.S.],
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Dimensionality reduction BibRef

Cardoso, Â.[Ângelo], Wichert, A.[Andreas],
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Clustering; K-means; High-dimensional data; Random projections BibRef

Leiva-Murillo, J.M.[José M.], Artés-Rodríguez, A.[Antonio],
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Kernel density estimation; Multivariate density modeling; Pattern recognition BibRef

Orlov, N.V.[Nikita V.], Eckley, D.M.[D. Mark], Shamir, L.[Lior], Goldberg, I.G.[Ilya G.],
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Dimensionality reduction; Self-taught learning; Joint sparse coding; Manifold learning; Unsupervised learning BibRef

Hacine-Gharbi, A.[Abdenour], Ravier, P.[Philippe], Harba, R.[Rachid], Mohamadi, T.[Tayeb],
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Mutual information; Feature selection; Bias; Dimensionality reduction; Shannon entropy; Speech recognition BibRef

Mu, T.T.[Ting-Ting], Goulermas, J.Y.[John Yannis], Tsujii, J.[Jun'ichi], Ananiadou, S.[Sophia],
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Mu, T.T.[Ting-Ting], Goulermas, J.Y.[John Yannis],
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Wu, Y.[Yu], Mu, T.T.[Ting-Ting], Liatsis, P.[Panos], Goulermas, J.Y.[John Y.],
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Co-embedding generation BibRef

Yu, J.[Jun], Tao, D.C.[Da-Cheng], Rui, Y.[Yong], Cheng, J.[Jun],
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Lai, Z.,
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PR(46), No. 6, June 2013, pp. 1523-1531.
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Large margin classifiers based on convex class models,
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IEEE DOI 0910
Large margin classifier; Classification; Convex approximation; Hyperdisk; Kernel method; Support Vector Machine BibRef

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Efficient object detection using cascades of nearest convex model classifiers,
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IEEE DOI 1208
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Cevikalp, H.[Hakan],
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IEEE DOI 1705
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2-Sided Best Fitting Hyperplane Classifier,
ICPR14(226-231)
IEEE DOI 1412
Accuracy Eigenvalues and eigenfunctions, Kernel, Object detection, Optimization, Support vector machines, Testing, Training, Best fitting hyperlane classifier, kernel methods, large margin classifier, open set recognition, support, vector, machines BibRef

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Cevikalp, H.[Hakan], Yavuz, H.S.[Hasan Serhan],
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Manifold learning; Multi-cluster manifolds; Nonlinear dimensionality reduction; Passage method BibRef

Kapoor, R., Gupta, R.,
Non-linear dimensionality reduction using fuzzy lattices,
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Kapoor, R., Gupta, R.,
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data visualisation BibRef

Jukic, A.[Ante], Filipovic, M.[Marko],
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Gao, Q., Gao, F., Zhang, H., Hao, X.J., Wang, X.,
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Dimensionality reduction BibRef

Tao, D.C.[Da-Cheng], Jin, L., Yang, Z., Li, X.L.[Xue-Long],
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IEEE DOI 1309
Dimension reduction classification. Using depth and low level features. BibRef

Gonen, M.,
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Bayes methods BibRef

Zhang, Z.[Zhao], Yan, S.C.[Shui-Cheng], Zhao, M.[Mingbo],
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convex programming BibRef

Zhu, L.[Lin], Huang, D.S.[De-Shuang],
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Dimensionality reduction BibRef

Wang, S.J.[Su-Jing], Yan, S.C.[Shui-Cheng], Yang, J.[Jian], Zhou, C.G.[Chun-Guang], Fu, X.L.[Xiao-Lan],
A General Exponential Framework for Dimensionality Reduction,
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IEEE DOI 1402
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Sun, W.W.[Wei-Wei], Halevy, A.[Avner], Benedetto, J.J.[John J.], Czaja, W.[Wojciech], Liu, C.[Chun], Wu, H.B.[Hang-Bin], Shi, B.[Beiqi], Li, W.Y.[Wei-Yue],
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Nonlinear dimensionality reduction BibRef

Yang, S., Jin, P., Li, B., Yang, L., Xu, W., Jiao, L.,
Semisupervised Dual-Geometric Subspace Projection for Dimensionality Reduction of Hyperspectral Image Data,
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Kim, K.[Kyoungok], Lee, J.W.[Jae-Wook],
Sentiment visualization and classification via semi-supervised nonlinear dimensionality reduction,
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Text visualization BibRef

He, J.R.[Jin-Rong], Ding, L.X.[Li-Xin], Jiang, L.[Lei], Li, Z.K.[Zhao-Kui], Hu, Q.H.[Qing-Hui],
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Intrinsic dimension estimation BibRef

Cui, Y.[Yan], Fan, L.[Liya],
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Wong, W.K.,
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Orsenigo, C.[Carlotta],
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Nonlinear dimensionality reduction BibRef

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Zhou, Y.C.[Yi-Cong], Peng, J.T.[Jiang-Tao], Chen, C.L.P.,
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geophysical image processing BibRef

Peng, J.T.[Jiang-Tao], Zhou, Y.C.[Yi-Cong], Chen, C.L.P.,
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Johnsson, K., Soneson, C., Fontes, M.,
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Song, M.P.[Mei-Ping], Chang, C.I.[Chein-I],
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geophysical image processing BibRef

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Kernel discriminant analysis (KDA) which operates in the reproducing kernel Hilbert space (RKHS). BibRef

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Prototype selection BibRef

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Kernel fusion-refinement BibRef

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Binary data BibRef

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IP(24), No. 12, December 2015, pp. 5684-5695.
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feature extraction BibRef

Zhao, K.[Kun], Alavi, A.[Azadeh], Wiliem, A.[Arnold], Lovell, B.C.[Brian C.],
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Riemannian manifolds BibRef

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Dimensionality reduction BibRef

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Multi-manifold learning BibRef

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IP(25), No. 5, May 2016, pp. 2407-2419.
IEEE DOI 1604
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Compact Representation for Image Classification: To Choose or to Compress?,
CVPR14(907-914)
IEEE DOI 1409
Correlation BibRef

Quispe, A.M.[Arturo Mendoza], Petitjean, C.[Caroline], Heutte, L.[Laurent],
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Elsevier DOI 1604
Dimensionality reduction BibRef

Liu, F.[Feng], Zhang, W.[Weijie], Gu, S.C.[Sui-Cheng],
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Elsevier DOI 1604
Manifold learning BibRef

Guo, X., Tie, Y., Qi, L., Guan, L.,
A Novel Semi-Supervised Dimensionality Reduction Framework,
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IEEE DOI 1605
Algorithm design and analysis BibRef

Wang, S.[Sheng], Lu, J.F.[Jian-Feng], Gu, X.J.[Xing-Jian], Du, H.S.[Hai-Shun], Yang, J.Y.[Jing-Yu],
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PR(57), No. 1, 2016, pp. 179-189.
Elsevier DOI 1605
Dimension reduction BibRef

Zhou, Z.J.[Zheng-Juan], Waqas, J.[Jadoon],
Intrinsic structure based feature transform for image classification,
JVCIR(38), No. 1, 2016, pp. 735-744.
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Dimensionality reduction BibRef

Najafi, A., Joudaki, A., Fatemizadeh, E.,
Nonlinear Dimensionality Reduction via Path-Based Isometric Mapping,
PAMI(38), No. 7, July 2016, pp. 1452-1464.
IEEE DOI 1606
Approximation algorithms BibRef

Yu, T., Zhang, W.,
Semisupervised Multilabel Learning With Joint Dimensionality Reduction,
SPLetters(23), No. 6, June 2016, pp. 795-799.
IEEE DOI 1606
computational complexity BibRef

Wen, J., Fowler, J.E., He, M., Zhao, Y.Q., Deng, C., Menon, V.,
Orthogonal Nonnegative Matrix Factorization Combining Multiple Features for Spectral-Spatial Dimensionality Reduction of Hyperspectral Imagery,
GeoRS(54), No. 7, July 2016, pp. 4272-4286.
IEEE DOI 1606
Computers BibRef

Kasun, L.L.C., Yang, Y., Huang, G.B., Zhang, Z.,
Dimension Reduction With Extreme Learning Machine,
IP(25), No. 8, August 2016, pp. 3906-3918.
IEEE DOI 1608
Additives BibRef

Zhao, W., Du, S.,
Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach,
GeoRS(54), No. 8, August 2016, pp. 4544-4554.
IEEE DOI 1608
feature extraction BibRef

Sakarya, U.[Ufuk],
Dimension reduction using global and local pattern information-based maximum margin criterion,
SIViP(10), No. 5, May 2016, pp. 903-909.
WWW Link. 1608
BibRef

Yu, M.Y.[Meng-Yang], Shao, L.[Ling], Zhen, X.T.[Xian-Tong], He, X.F.[Xiao-Fei],
Local Feature Discriminant Projection,
PAMI(38), No. 9, September 2016, pp. 1908-1914.
IEEE DOI 1609
supervised dimensionality reduction of local features. feature extraction BibRef

Li, J.[Jun], Kong, Y.[Yu], Zhao, H.D.[Han-Dong], Yang, J.[Jian], Fu, Y.[Yun],
Learning Fast Low-Rank Projection for Image Classification,
IP(25), No. 10, October 2016, pp. 4803-4814.
IEEE DOI 1610
image classification BibRef

Huang, K.K.[Ke-Kun], Dai, D.Q.[Dao-Qing], Ren, C.X.[Chuan-Xian],
Regularized coplanar discriminant analysis for dimensionality reduction,
PR(62), No. 1, 2017, pp. 87-98.
Elsevier DOI 1705
Dimensionality reduction BibRef

Chen, P.[Puhua], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Zhao, J.[Jiaqi], Zhao, Z.Q.[Zhi-Qiang], Liu, S.[Shuai],
Semi-supervised double sparse graphs based discriminant analysis for dimensionality reduction,
PR(61), No. 1, 2017, pp. 361-378.
Elsevier DOI 1705
Semi-supervised learning BibRef

Casalino, G.[Gabriella], Gillis, N.[Nicolas],
Sequential dimensionality reduction for extracting localized features,
PR(63), No. 1, 2017, pp. 15-29.
Elsevier DOI 1612
Nonnegative matrix factorization BibRef

Wang, Y.[Yong], Xie, J.B.[Jian-Bin], Wu, Y.[Yi],
Orthogonal discriminant analysis revisited,
PRL(84), No. 1, 2016, pp. 149-155.
Elsevier DOI 1612
Dimensionality reduction BibRef

Song, S., Gong, Y., Zhang, Y., Huang, G., Huang, G.B.,
Dimension Reduction by Minimum Error Minimax Probability Machine,
SMCS(47), No. 1, January 2017, pp. 58-69.
IEEE DOI 1612
Covariance matrices BibRef

Zhang, C., Fu, H., Hu, Q., Zhu, P., Cao, X.,
Flexible Multi-View Dimensionality Co-Reduction,
IP(26), No. 2, February 2017, pp. 648-659.
IEEE DOI 1702
Hilbert spaces BibRef

Shao, G.[Guowan], Sang, N.[Nong],
Regularized max-min linear discriminant analysis,
PR(66), No. 1, 2017, pp. 353-363.
Elsevier DOI 1704
BibRef
Earlier:
Fractional-step max-min distance analysis for dimension reduction,
ICPR12(396-400).
WWW Link. 1302
Dimensionality reduction BibRef

Yuan, S., Mao, X., Chen, L.,
Multilinear Spatial Discriminant Analysis for Dimensionality Reduction,
IP(26), No. 6, June 2017, pp. 2669-2681.
IEEE DOI 1705
encoding, principal component analysis, MSDA, MSDA model, Weizmann action database, dimensionality reduction, encoding multidimensional data, linear projection technique, multilinear linear discriminant analysis, multilinear principal component analysis, multilinear projection technique, multilinear spatial discriminant analysis, tensor locality preserving projection, theoretical analysis, Algorithm design and analysis, Classification algorithms, Face, Face recognition, Manifolds, Principal component analysis, Tensile stress, Dimensionality reduction, face recognition, high-order tensor, multilinear principal component analysis, spatial, discriminant, characteristic BibRef

Wong, W.K., Lai, Z., Wen, J., Fang, X., Lu, Y.,
Low-Rank Embedding for Robust Image Feature Extraction,
IP(26), No. 6, June 2017, pp. 2905-2917.
IEEE DOI 1705
Algorithm design and analysis, Eigenvalues and eigenfunctions, Feature extraction, Image reconstruction, Manifolds, Principal component analysis, Robustness, Robust linear dimensionality reduction, image feature extraction, low rank representation, subspace, learning BibRef

Taskin, G., Kaya, H., Bruzzone, L.,
Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images,
IP(26), No. 6, June 2017, pp. 2918-2928.
IEEE DOI 1705
Computational efficiency, Computational modeling, Correlation, Feature extraction, Hyperspectral imaging, Kernel, Training, Dimensionality reduction, feature selection, high dimensional model representation, hyperspectral, image, classification BibRef

Niu, G.[Guo], Ma, Z.M.[Zheng-Ming],
Local non-linear alignment for non-linear dimensionality reduction,
IET-CV(11), No. 5, August 2017, pp. 331-341.
DOI Link 1707
BibRef

Liu, H., Liu, L., Le, T.D., Lee, I., Sun, S., Li, J.,
Nonparametric Sparse Matrix Decomposition for Cross-View Dimensionality Reduction,
MultMed(19), No. 8, August 2017, pp. 1848-1859.
IEEE DOI 1708
Biological system modeling, Correlation, Covariance matrices, Learning systems, Matrix decomposition, Principal component analysis, Sparse matrices, Cross-view data, dimension reduction, matrix decomposition, sparse learning, sparsity-inducing, function BibRef

Wang, R., Nie, F., Hong, R., Chang, X., Yang, X., Yu, W.,
Fast and Orthogonal Locality Preserving Projections for Dimensionality Reduction,
IP(26), No. 10, October 2017, pp. 5019-5030.
IEEE DOI 1708
Eigenvalues and eigenfunctions, Face recognition, Laplace equations, Manifolds, Optimization, Training data, Dimensionality reduction (DR), hyperspectral image (HSI) classification, locality preserving projections, (LPP) BibRef


Hofmeyr, D.P.,
Clustering by Minimum Cut Hyperplanes,
PAMI(39), No. 8, August 2017, pp. 1547-1560.
IEEE DOI 1707
Clustering algorithms, Clustering methods, Context, Particle separators, Partitioning algorithms, Probability distribution, Clustering, asymptotics, hyperplane, maximum margin, normalised, cut BibRef

Raytchev, B., Katamoto, Y., Koujiba, M., Tamaki, T., Kaneda, K.,
Ensemble-based local learning for high-dimensional data regression,
ICPR16(2640-2645)
IEEE DOI 1705
Bagging, Computed tomography, Data models, Histograms, Learning systems, Linear regression, Training BibRef

Yoshiyasu, Y., Yoshida, E.,
Nonlinear dimensionality reduction by curvature minimization,
ICPR16(3590-3596)
IEEE DOI 1705
Distortion, Laplace equations, Manifolds, Minimization, Optimization, Three-dimensional displays, Transmission, line, matrix, methods BibRef

Chung, A.G., Shafiee, M.J., Wong, A.,
Random feature maps via a Layered Random Projection (LARP) framework for object classification,
ICIP16(246-250)
IEEE DOI 1610
Databases BibRef

Rui, L., Nejati, H., Cheung, N.M.,
Dimensionality reduction of brain imaging data using graph signal processing,
ICIP16(1329-1333)
IEEE DOI 1610
Brain BibRef

Huang, S., Tran, T.D.,
Dimensionality reduction for image classification via mutual information maximization,
ICIP16(509-513)
IEEE DOI 1610
Eigenvalues and eigenfunctions BibRef

Kirishanthy, T., Ramanan, A.,
Creating Compact and Discriminative Visual Vocabularies Using Visual Bits,
DICTA15(1-6)
IEEE DOI 1603
Map the low-level features into a fixed-length vector in histogram space and applied classifiers. BibRef

Zhang, X.[Xu], Yu, F.X.[Felix X.], Guo, R.Q.[Rui-Qi], Kumar, S.[Sanjiv], Wang, S.J.[Sheng-Jin], Chang, S.F.[Shi-Fu],
Fast Orthogonal Projection Based on Kronecker Product,
ICCV15(2929-2937)
IEEE DOI 1602
Binary codes. Projetions of high dimensional data. BibRef

Fang, X.Z.[Xiao-Zhao], Xu, Y.[Yong], Zhang, Z.[Zheng], Lai, Z.H.[Zhi-Hui], Shen, L.L.[Lin-Lin],
Orthogonal self-guided similarity preserving projections,
ICIP15(344-348)
IEEE DOI 1512
dimensionality reduction; similarity preserving; sparse coding BibRef

Zhang, L.[Lei], Peng, P.[Peipei], Xiang, X.Z.[Xue-Zhi], Zhen, X.T.[Xian-Tong],
Dimensionality reduction by supervised locality analysis,
ICIP15(1488-1492)
IEEE DOI 1512
Dimensionality reduction BibRef

Czolombitko, M.[Michal], Stepaniuk, J.[Jaroslaw],
Generating Core Based on Discernibility Measure and MapReduce,
PReMI15(367-376).
Springer DOI 1511
BibRef

Honko, P.[Piotr],
Scalability of Data Decomposition Based Algorithms: Attribute Reduction Problem,
PReMI15(387-396).
Springer DOI 1511
BibRef

Düntsch, I.[Ivo], Gediga, G.[Günther],
Simplifying Contextual Structures,
PReMI15(23-32).
Springer DOI 1511
ICRA BibRef

Campadelli, P.[Paola], Casiraghi, E.[Elena], Ceruti, C.[Claudio],
Neighborhood Selection for Dimensionality Reduction,
CIAP15(I:183-191).
Springer DOI 1511
BibRef

Banerjee, M.[Minakshi], Islam, S.M.[Seikh Mazharul],
Tackling Curse of Dimensionality for Efficient Content Based Image Retrieval,
PReMI15(149-158).
Springer DOI 1511
BibRef

Wang, S., Wang, C.,
Research on dimension reduction method for hyperspectral remote sensing image based on global mixture coordination factor analysis,
IWIDF15(159-167).
DOI Link 1508
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Masoudimansour, W.[Walid], Bouguila, N.[Nizar],
Dimensionality Reduction of Proportional Data Through Data Separation Using Dirichlet Distribution,
ICIAR15(141-149).
Springer DOI 1507
BibRef

Qiu, Q.A.[Qi-Ang], Sapiro, G.[Guillermo],
Learning compressed image classification features,
ICIP14(5761-5765)
IEEE DOI 1502
Accuracy; Face; Image coding; Optimization; Testing; Training; Transforms BibRef

Zhao, Z.[Zhong], Feng, G.[Guocan],
A Dictionary-Based Algorithm for Dimensionality Reduction and Data Reconstruction,
ICPR14(1556-1561)
IEEE DOI 1412
Algorithm design and analysis BibRef

Nie, S.[Siqi], Ji, Q.A.[Qi-Ang],
Feature Learning Using Bayesian Linear Regression Model,
ICPR14(1502-1507)
IEEE DOI 1412
Accuracy BibRef

Huang, Y.[Yan], Wang, W.[Wei], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
A General Nonlinear Embedding Framework Based on Deep Neural Network,
ICPR14(732-737)
IEEE DOI 1412
Face BibRef

Huang, P.[Peihao], Huang, Y.[Yan], Wang, W.[Wei], Wang, L.[Liang],
Deep Embedding Network for Clustering,
ICPR14(1532-1537)
IEEE DOI 1412
Clustering algorithms BibRef

Wang, W.[Wei], Huang, Y.[Yan], Wang, Y.[Yizhou], Wang, L.[Liang],
Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction,
DeepLearn14(496-503)
IEEE DOI 1409
Autoencoder; Deep learning; Dimensionality reduction BibRef

Huang, S.[Sheng], Elgammal, A.M.[Ahmed M.], Huangfu, L.[Luwen], Yang, D.[Dan], Zhang, X.H.[Xiao-Hong],
Globality-Locality Preserving Projections for Biometric Data Dimensionality Reduction,
Biometrics14(15-20)
IEEE DOI 1409
Dimensionality Reduction BibRef

Zhao, B.[Bin], Xing, E.P.[Eric P.],
Hierarchical Feature Hashing for Fast Dimensionality Reduction,
CVPR14(2051-2058)
IEEE DOI 1409
BibRef

Floyd, D., Cloutier, R., Zigh, T.,
Nonlinear dimensionality reduction for structural discovery in image processing,
AIPR13(1-6)
IEEE DOI 1408
image processing BibRef

Turki, T.[Turki], Roshan, U.[Usman],
Weighted Maximum Variance Dimensionality Reduction,
MCPR14(11-20).
Springer DOI 1407
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Wang, Y.M.[Ya-Ming], Morariu, V.I.[Vlad I.], Davis, L.S.[Larry S.],
Unsupervised Feature Extraction Inspired by Latent Low-Rank Representation,
WACV15(542-549)
IEEE DOI 1503
Algorithm design and analysis BibRef

Morariu, V.I.[Vlad I.], Ahmed, E.[Ejaz], Santhanam, V.[Venkataraman], Harwood, D.[David], Davis, L.S.[Larry S.],
Composite Discriminant Factor analysis,
WACV14(564-571)
IEEE DOI 1406
Accuracy BibRef

Martin, S., Szymanski, L.,
Singularity resolution for dimension reduction,
IVCNZ13(19-24)
IEEE DOI 1402
algebra BibRef

Su, B.[Bing], Ding, X.Q.[Xiao-Qing],
Linear Sequence Discriminant Analysis: A Model-Based Dimensionality Reduction Method for Vector Sequences,
ICCV13(889-896)
IEEE DOI 1403
BibRef

Liu, L.Q.[Ling-Qiao], Wang, L.[Lei],
A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-Dimensional Visual Data,
ICCV13(3008-3015)
IEEE DOI 1403
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Zhong, G.Q.[Guo-Qiang], Chherawala, Y., Cheriet, M.,
An Empirical Evaluation of Supervised Dimensionality Reduction for Recognition,
ICDAR13(1315-1319)
IEEE DOI 1312
document image processing BibRef

Campadelli, P.[Paola], Casiraghi, E.[Elena],
Local Intrinsic Dimensionality Based Features for Clustering,
CIAP13(I:41-50).
Springer DOI 1311
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Bull, G., Gao, J.B.[Jun-Bin],
Transposed Low Rank Representation for Image Classification,
DICTA12(1-7).
IEEE DOI 1303
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Hino, H.[Hideitsu], Wakayama, K.[Keigo], Murata, N.[Noboru],
Sliced inverse regression with conditional entropy minimization,
ICPR12(1185-1188).
WWW Link. 1302
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Zheng, Y.[Yali], Tang, Y.Y.[Yuan Yan], Fang, B.[Bin], Zhang, T.P.[Tai-Ping],
Orthogonal Isometric Projection,
ICPR12(405-408).
WWW Link. 1302
Orthogonal Isometric Projection for dimensionality reduction. BibRef

Liu, X.[Xi], Liu, R.J.[Ru-Jie], Li, F.[Fei], Cao, Q.[Qiong],
Graph-based dimensionality reduction for KNN-based image annotation,
ICPR12(1253-1256).
WWW Link. 1302
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Liao, W.H.[Wen-Hung],
Commensurate dimensionality reduction for extended local ternary patterns,
ICPR12(3013-3016).
WWW Link. 1302
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Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
Confused Distance Maximization for Large Category Dimensionality Reduction,
FHR12(213-218).
IEEE DOI 1302
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Xu, B.[Bo], Huang, K.Z.[Kai-Zhu], Liu, C.L.[Cheng-Lin],
Dimensionality Reduction by Minimal Distance Maximization,
ICPR10(569-572).
IEEE DOI 1008
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Raducanu, B.[Bogdan], Dornaika, F.[Fadi],
Out-of-Sample Embedding by Sparse Representation,
SSSPR12(336-344).
Springer DOI 1211
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Hariharan, B.[Bharath], Malik, J.[Jitendra], Ramanan, D.[Deva],
Discriminative Decorrelation for Clustering and Classification,
ECCV12(IV: 459-472).
Springer DOI 1210
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Nguyen, H.V.[Hien V.], Patel, V.M.[Vishal M.], Nasrabadi, N.M.[Nasser M.], Chellappa, R.[Rama],
Sparse Embedding: A Framework for Sparsity Promoting Dimensionality Reduction,
ECCV12(VI: 414-427).
Springer DOI 1210
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Hong, Y.[Yi], Li, Q.N.[Quan-Nan], Jiang, J.Y.[Jia-Yan], Tu, Z.W.[Zhuo-Wen],
Learning a mixture of sparse distance metrics for classification and dimensionality reduction,
ICCV11(906-913).
IEEE DOI 1201
neighborhood components analysis. Mixture of sparse metrics BibRef

Gan, L.[Lu], Do, T.T.[Thong T.], Tran, T.D.[Trac D.],
Fast dimension reduction through random permutation,
ICIP10(3353-3356).
IEEE DOI 1009
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Sankaran, P.[Praveen], Asari, V.[Vijayan],
A second order polynomial based subspace projection method for dimensionality reduction,
ICIP10(3857-3860).
IEEE DOI 1009
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Sulic, V.[Vildana], Pers, J.[Janez], Kristan, M.[Matej], Kovacic, S.[Stanislav],
Dimensionality Reduction for Distributed Vision Systems Using Random Projection,
ICPR10(380-383).
IEEE DOI 1008
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Aouada, D.[Djamila], Baryshnikov, Y.[Yuliy], Krim, H.[Hamid],
Mahalanobis-based Adaptive Nonlinear Dimension Reduction,
ICPR10(742-745).
IEEE DOI 1008
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Hussain, S.U.[Sibt-Ul], Triggs, B.[Bill],
Feature Sets and Dimensionality Reduction for Visual Object Detection,
BMVC10(xx-yy).
HTML Version. 1009
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Shyr, A.[Alex], Urtasun, R.[Raquel], Jordan, M.I.[Michael I.],
Sufficient dimension reduction for visual sequence classification,
CVPR10(3610-3617).
IEEE DOI 1006
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Wang, P.[Peng], Shen, C.H.[Chun-Hua], Zheng, H.[Hong], Ren, Z.[Zhang],
A Variant of the Trace Quotient Formulation for Dimensionality Reduction,
ACCV09(III: 277-286).
Springer DOI 0909
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Wu, D.[Di], Zhou, Z.L.[Zi-Li], Feng, S.J.[Shui-Juan], He, Y.[Yong],
Uninformation Variable Elimination and Successive Projections Algorithm in Mid-Infrared Spectral Wavenumber Selection,
CISP09(1-5).
IEEE DOI 0910
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Sun, Y.K.[Ying-Kai], Chen, H.[Hai],
Application of Rough Set in Image's Feature Attributes Reduction,
CISP09(1-4).
IEEE DOI 0910
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Xu, X.L.[Xiao-Li], Chen, T.[Tao],
ISOMAP Algorithm-Based Feature Extraction for Electromechanical Equipment Fault Prediction,
CISP09(1-4).
IEEE DOI 0910
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Bauckhage, C.[Christian], Thurau, C.[Christian],
Adapting Information Theoretic Clustering to Binary Images,
ICPR10(910-913).
IEEE DOI 1008
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Earlier:
Making Archetypal Analysis Practical,
DAGM09(272-281).
Springer DOI 0909
Represent as combination of extremal points. BibRef

Thurau, C.[Christian],
Nearest Archetype Hull Methods for Large-Scale Data Classification,
ICPR10(4040-4043).
IEEE DOI 1008
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Sáenz, C.[Carlos], Hernández, B.[Begońa], Alberdi, C.[Coro], Alfonso, S.[Santiago], Dińeiro, J.M.[José Manuel],
The Number of Linearly Independent Vectors in Spectral Databases,
SCIA09(570-579).
Springer DOI 0906
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Gu, G.D.[Guo-Dong], Zhang, Y.S.[Yong-Shun], Hu, J.H.[Jun-Hong], Shen, K.[Kai],
An attribute fast reduction algorithm based on modified discernable matrix of S-rough sets,
IASP09(366-368).
IEEE DOI 0904
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Jeong, S.D.[Seung-Do], Kim, S.W.[Sang-Wook], Kim, W.Y.[Whoi-Yul], Choi, B.U.[Byung-Uk],
Effective dimensionality reduction in multimedia applications,
CIIP09(82-87).
IEEE DOI 0903
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Hui, K.H.[Kang-Hua], Wang, C.H.[Chun-Heng],
Clustering-based locally linear embedding,
ICPR08(1-4).
IEEE DOI 0812
LLE for dimensionality reduction BibRef

Kashima, H.[Hisashi], Yamasaki, K.[Kazutaka], Inokuchi, A.[Akihiro], Saigo, H.[Hiroto],
Regression with interval output values,
ICPR08(1-4).
IEEE DOI 0812
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Jia, Y.Q.[Yang-Qing], Zhang, C.S.[Chang-Shui],
Local Regularized Least-Square Dimensionality Reduction,
ICPR08(1-4).
IEEE DOI 0812
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Mahmoudi, M.[Mona], Vandergheynst, P.[Pierre], Sorci, M.[Matteo],
On the estimation of geodesic paths on sampled manifolds under random projections,
ICIP08(1840-1843).
IEEE DOI 0810
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Marinai, S.[Simone], Marino, E.[Emanuele], Soda, G.[Giovanni],
Nonlinear Embedded Map Projection for Dimensionality Reduction,
CIAP09(219-228).
Springer DOI 0909
BibRef
Earlier:
Embedded Map Projection for Dimensionality Reduction-Based Similarity Search,
SSPR08(582-591).
Springer DOI 0812
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Ribeiro, B.[Bernardete], Vieira, A.[Armando], Carvalho das Neves, J.[Joăo],
Supervised Isomap with Dissimilarity Measures in Embedding Learning,
CIARP08(389-396).
Springer DOI 0809
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Mordohai, P.[Philippos], Medioni, G.,
Unsupervised dimensionality estimation and manifold learning in high-dimensional spaces by tensor voting,
IJCAI05(798-803).
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Goh, A.[Alvina], Vidal, R.[Rene],
Clustering and dimensionality reduction on Riemannian manifolds,
CVPR08(1-7).
IEEE DOI 0806
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Carreira-Perpinan, M.A.[Miguel A.], Lu, Z.D.[Zheng-Dong],
Parametric dimensionality reduction by unsupervised regression,
CVPR10(1895-1902).
IEEE DOI 1006
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Earlier:
Dimensionality reduction by unsupervised regression,
CVPR08(1-8).
IEEE DOI 0806
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Shen, C.H.[Chun-Hua], Li, H.D.[Hong-Dong], Brooks, M.J.[Michael J.],
A Convex Programming Approach to the Trace Quotient Problem,
ACCV07(II: 227-235).
Springer DOI 0711
Apply to manifold learning, low-dimension embedding. BibRef

Li, J.[Jun], Hao, P.W.[Peng-Wei],
Reliable Representation of Data on Manifolds,
BMVC08(xx-yy).
PDF File. 0809
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Earlier:
Hierarchical Structuring of Data on Manifolds,
CVPR07(1-8).
IEEE DOI 0706
For new sample, find landmark points for classification. BibRef

Chen, J.H.[Jian-Hui], Ye, J.P.[Jie-Ping], Li, Q.[Qi],
Integrating Global and Local Structures: A Least Squares Framework for Dimensionality Reduction,
CVPR07(1-8).
IEEE DOI 0706
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Yang, Z.R.[Zhi-Rong], Laaksonen, J.T.[Jorma T.],
Regularized Neighborhood Component Analysis,
SCIA07(253-262).
Springer DOI 0706
Neighborhood Component Analysis and Relevant Component Analysis. BibRef

Li, Y.Z.[Yong-Zhi], Ming, F.[Feng], Yang, J.Y.[Jing-Yu], Pan, R.L.[Ren-Liang],
An Efficient Method of Nonlinear Feature Extraction Based on SVM,
ICARCV06(1-6).
IEEE DOI 0612
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Song, F.X.[Feng-Xi], Zhang, D.[David], Chen, Q.L.[Qing-Long], Yang, J.Y.[Jing-Yu],
A Novel Supervised Dimensionality Reduction Algorithm for Online Image Recognition,
PSIVT06(198-207).
Springer DOI 0612
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Dixon, M.[Michael], Jacobs, N.[Nathan], Pless, R.[Robert],
Finding Minimal Parameterizations of Cylindrical Image Manifolds,
PercOrg06(192).
IEEE DOI 0609
High dimensional data that vary due to a few parameters. BibRef

Yan, S.C.[Shui-Cheng], Tang, X.[Xiaoou],
Dimensionality Reduction with Adaptive Kernels,
ICPR06(II: 626-629).
IEEE DOI 0609
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Chen, H.F.[Hai-Feng], Jiang, G.[Guofei], Yoshihira, K.[Kenji],
Robust Nonlinear Dimensionality Reduction for Manifold Learning,
ICPR06(II: 447-450).
IEEE DOI 0609
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Yu, K.[Kai], Yu, S.P.[Shi-Peng], Tresp, V.[Volker],
Multi-Output Regularized Projection,
CVPR05(II: 597-602).
IEEE DOI 0507
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Wolf, L.B.[Lior B.], Bileschi, S.M.[Stan M.],
Combining Variable Selection with Dimensionality Reduction,
CVPR05(II: 801-806).
IEEE DOI 0507
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Andersson, F.[Fredrik], Nilsson, J.[Jens],
Nonlinear Dimensionality Reduction Using Circuit Models,
SCIA05(950-959).
Springer DOI 0506
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Trujillo, M., Sadki, M.,
Correspondence analysis applied to textural features recognition,
Southwest04(119-123).
WWW Link. 0411
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Brown, M., Costen, N.P., Akamatsu, S.,
Efficient calculation of the complete optimal classification set,
ICPR04(II: 307-310).
IEEE DOI 0409
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Shimano, M., Nagao, K.,
Simultaneous optimization of class configuration and feature space for object recognition,
ICPR04(II: 7-10).
IEEE DOI 0409
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Wang, J.[Jia], Lu, H.Q.[Han-Qing], Liu, Q.S.[Qing-Shan],
Feature space analysis using low-order tensor voting,
ICIP04(IV: 2681-2684).
IEEE DOI 0505
BibRef
And:
Tensor voting toward feature space analysis,
ICPR04(III: 462-465).
IEEE DOI 0409
BibRef

Dasarathy, B.V., Sánchez, J.S.,
Tandem Fusion of Nearest Neighbor Editing and Condensing Algorithms: Data Dimensionality Effects,
ICPR00(Vol II: 692-695).
IEEE DOI 0009
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Piper, J., Poole, I., Carothers, A.,
Stein's paradox and improved quadratic discrimination of real and simulated data by covariance weighting,
ICPR94(B:529-532).
IEEE DOI 9410
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Valev, V.,
On the representation of training tables in a K-valued code and the construction of empirical regularities,
ICPR88(II: 779-781).
IEEE DOI 8811
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Spectral Clustering, Data Dimensionality Reduction .


Last update:Sep 18, 2017 at 11:34:11