14.1.4 Number of Features, Dimensionality Reduction

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
BibRef

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
BibRef

Xue, H.[Hui], Chen, S.C.[Song-Can], Zeng, X.Q.[Xiao-Qin],
Classifier learning with a new locality regularization method,
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

Guo, Y.[Yi], Gao, J.B.[Jun-Bin], Kwan, P.W.[Paul W.],
Twin Kernel Embedding,
PAMI(30), No. 8, August 2008, pp. 1490-1495.
IEEE DOI 0806
BibRef

Rueda, L.G.[Luis G.], Herrera, M.[Myriam],
Linear dimensionality reduction by maximizing the Chernoff distance in the transformed space,
PR(41), No. 10, October 2008, pp. 3138-3152.
Elsevier DOI 0808
BibRef
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.
Elsevier DOI 0811
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:
Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction,
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],
A scalable dual approach to semidefinite metric learning,
CVPR11(2601-2608).
IEEE DOI 1106
BibRef

Scoleri, T., Chojnacki, W., Brooks, M.J.[Michael J.],
Dimensionality reduction for more stable vision parameter estimation,
IET-CV(2), No. 4, December 2008, pp. 218-227.
DOI Link 0905
BibRef

Scoleri, T.,
Post-hoc Correction Techniques for Constrained Parameter Estimation in Computer Vision,
DICTA08(412-419).
IEEE DOI 0812
BibRef

Nie, F.P.[Fei-Ping], Xiang, S.M.[Shi-Ming], Song, Y.Q.[Yang-Qiu], Zhang, C.S.[Chang-Shui],
Extracting the optimal dimensionality for local tensor discriminant analysis,
PR(42), No. 1, January 2009, pp. 105-114.
Elsevier DOI 0809
Optimal dimensionality, Local scatter, Tensor discriminant analysis; Alternating optimization BibRef

Hou, C., Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.,
Learning an Orthogonal and Smooth Subspace for Image Classification,
SPLetters(16), No. 4, April 2009, pp. 303-306.
IEEE DOI 0903
BibRef

Hou, C., Nie, F.P., Yi, D., Wu, Y.,
Efficient Image Classification via Multiple Rank Regression,
IP(22), No. 1, January 2013, pp. 340-352.
IEEE DOI 1301
BibRef

Liu, Y.[Yang], Liu, Y.[Yan], Chan, K.C.C.[Keith C.C.],
Dimensionality reduction for heterogeneous dataset in rushes editing,
PR(42), No. 2, February 2009, pp. 229-242.
Elsevier DOI 0810
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.],
Tensor-based locally maximum margin classifier for image and video classification,
CVIU(115), No. 3, March 2011, pp. 300-309.
Elsevier DOI 1103
BibRef
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],
Enhancing Bilinear Subspace Learning by Element Rearrangement,
PAMI(31), No. 10, October 2009, pp. 1913-1920.
IEEE DOI 0909
BibRef

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
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
BibRef

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
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.X.[Zhi-Xia], 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

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
BibRef

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

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

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

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

Wang, J.Z.[Jian-Zhong], Zhang, B.[Baoxue], Qi, M.[Miao], Kong, J.[Jun],
Linear discriminant projection embedding based on patches alignment,
IVC(28), No. 12, December 2010, pp. 1624-1636.
Elsevier DOI 1003
Dimensionality reduction, Manifold learning, Patches alignment, Face recognition, Maximum margin criterion BibRef

Kaban, A.[Ata],
On the distance concentration awareness of certain data reduction techniques,
PR(44), No. 2, February 2011, pp. 265-277.
Elsevier DOI 1011
Distance concentration, Dimensionality reduction, Feature selection; Projection pursuit, Sure independence screening BibRef

Zhang, P.[Peng], Qiao, H.[Hong], Zhang, B.[Bo],
An improved local tangent space alignment method for manifold learning,
PRL(32), No. 2, 15 January 2011, pp. 181-189.
Elsevier DOI 1101
Nonlinear dimensionality reduction, Manifold learning, Data mining BibRef

Salamo, M.[Maria], Lopez-Sanchez, M.[Maite],
Rough set based approaches to feature selection for Case-Based Reasoning classifiers,
PRL(32), No. 2, 15 January 2011, pp. 280-292.
Elsevier DOI 1101
Feature selection, Dimensionality reduction, Classification techniques, Case-Based Reasoning, Rough Set Theory BibRef

Villegas, M.[Mauricio], Paredes, R.[Roberto],
Dimensionality reduction by minimizing nearest-neighbor classification error,
PRL(32), No. 4, 1 March 2011, pp. 633-639.
Elsevier DOI 1102
Dimensionality reduction, Nearest-neighbor classifier BibRef

Shang, F.H.[Fan-Hua], Jiao, L.C., Shi, J.R.[Jia-Rong], Chai, J.[Jing],
Robust Positive semidefinite L-Isomap Ensemble,
PRL(32), No. 4, 1 March 2011, pp. 640-649.
Elsevier DOI 1102
Dimensionality reduction, Manifold learning, Nystrom approximation; Isomap, Ensemble learning, High dimensional affine transformation BibRef

Kim, M.Y.[Min-Young], Pavlovic, V.[Vladimir],
Central Subspace Dimensionality Reduction Using Covariance Operators,
PAMI(33), No. 4, April 2011, pp. 657-670.
IEEE DOI 1103
BibRef
Earlier:
Dimensionality reduction using covariance operator inverse regression,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Kim, M.Y.[Min-Young],
Time-Series Dimensionality Reduction via Granger Causality,
SPLetters(19), No. 10, October 2012, pp. 611-614.
IEEE DOI 1209
BibRef

Gao, X., Wang, X., Tao, D., Li, X.,
Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction,
SMC-B(41), No. 2, April 2011, pp. 425-434.
IEEE DOI 1103
BibRef

Xiong, H., Liu, T., Tao, D., Shen, H.T.,
Dual Diversified Dynamical Gaussian Process Latent Variable Model for Video Repairing,
IP(25), No. 8, August 2016, pp. 3626-3637.
IEEE DOI 1608
Gaussian processes BibRef

Wu, J., Zhang, X.L.,
Maximum Margin Clustering Based Statistical VAD With Multiple Observation Compound Feature,
SPLetters(18), No. 5, May 2011, pp. 283-286.
IEEE DOI 1103
BibRef

Park, H.[Heum], Kwon, H.C.[Hyuk-Chul],
Improved Gini-Index Algorithm to Correct Feature-Selection Bias in Text Classification,
IEICE(E94-D), No. 4, April 2011, pp. 855-865.
WWW Link. 1104
Gini-index used for splitting measure in decision tree. BibRef

Chang, C.I.[Chein-I], Safavi, H.[Haleh],
Progressive dimensionality reduction by transform for hyperspectral imagery,
PR(44), No. 10-11, October-November 2011, pp. 2760-2773.
Elsevier DOI 1101
Backward progressive dimensionality reduction by PI-PP (BPDR-PIPP); Dimensionality reduction by transform (DRT), Forward progressive dimensionality reduction by PI-PP (FPDR-PIPP), Progressive dimensionality reduction by projection index-based projection pursuit (PDR-PIPP), Progressive dimensionality reduction by transform (PDRT); Projection index-based projection pursuit (PIPP) BibRef

Wong, W.K., Zhao, H.T.,
Supervised optimal locality preserving projection,
PR(45), No. 1, January 2012, pp. 186-197.
Elsevier DOI 1109
Classification, Feature extraction, Dimensionality reduction, Manifold learning BibRef

Lai, Z.H.[Zhi-Hui], Jin, Z.[Zhong], Wong, W.K.,
Tangent space discriminant analysis for feature extraction,
ICIP10(3793-3796).
IEEE DOI 1009
BibRef

van de Ville, D., Kocher, M.,
Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection,
IP(20), No. 9, September 2011, pp. 2683-2690.
IEEE DOI 1109
BibRef

Lázaro-Gredilla, M.[Miguel], van Vaerenbergh, S.[Steven], Lawrence, N.D.[Neil D.],
Overlapping Mixtures of Gaussian Processes for the data association problem,
PR(45), No. 4, April 2012, pp. 1386-1395.
Elsevier DOI 1112
Gaussian Processes, Marginalized variational inference, Bayesian models BibRef

Urtasun, R.[Raquel], Quattoni, A.[Ariadna], Lawrence, N.D.[Neil D.], Darrell, T.J.[Trevor J.],
Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space,
CSAIL-2008-020, April 2008.
WWW Link. BibRef 0804

Alvarez, M.A.[Mauricio A.], Luengo, D.[David], Lawrence, N.D.[Neil D.],
Linear Latent Force Models Using Gaussian Processes,
PAMI(35), No. 11, 2013, pp. 2693-2705.
IEEE DOI 1309
Gaussian processes. BibRef

Geiger, A.[Andreas], Urtasun, R.[Raquel], Darrell, T.J.[Trevor J.], Stiefelhagen, R.[Rainer],
Rank Priors for Continuous Non-Linear Dimensionality Reduction,
CSAIL-2008-056, September 2008.
WWW Link. BibRef 0809
And: A1, A2, A3, Only: CVPR09(880-887).
IEEE DOI 0906
BibRef

Zhang, Z.[Zhao], Zhao, M.B.[Ming-Bo], Chow, T.W.S.[Tommy W.S.],
Constrained large Margin Local Projection algorithms and extensions for multimodal dimensionality reduction,
PR(45), No. 12, December 2012, pp. 4466-4493.
Elsevier DOI 1208
Dimensionality reduction, Large margin projection, Manifold visualization, Pairwise constraints, Locality preservation; Multimodality preservation, Kernel method, Pattern classification BibRef

Orlov, N.V.[Nikita V.], Eckley, D.M.[D. Mark], Shamir, L.[Lior], Goldberg, I.G.[Ilya G.],
Improving class separability using extended pixel planes: A comparative study,
MVA(23), No. 5, September 2012, pp. 1047-1058.
WWW Link. 1208
Using transforms. BibRef

Hacine-Gharbi, A.[Abdenour], Ravier, P.[Philippe], Harba, R.[Rachid], Mohamadi, T.[Tayeb],
Low bias histogram-based estimation of mutual information for feature selection,
PRL(33), No. 10, 15 July 2012, pp. 1302-1308.
Elsevier DOI 1205
Mutual information, Feature selection, Bias, Dimensionality reduction; Shannon entropy, Speech recognition BibRef

Hacine-Gharbi, A.[Abdenour], Ravier, P.[Philippe],
A binning formula of bi-histogram for joint entropy estimation using mean square error minimization,
PRL(101), No. 1, 2018, pp. 21-28.
Elsevier DOI 1801
Histogram bin number BibRef

Mu, T.T.[Ting-Ting], Goulermas, J.Y.[John Yannis],
Automatic Generation of Co-Embeddings from Relational Data with Adaptive Shaping,
PAMI(35), No. 10, 2013, pp. 2340-2356.
IEEE DOI 1309
Relational data BibRef

Hofmeyr, D.P.[David 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

Wu, Y.[Yu], Mu, T.T.[Ting-Ting], Liatsis, P.[Panos], Goulermas, J.Y.[John Y.],
Computation of heterogeneous object co-embeddings from relational measurements,
PR(65), No. 1, 2017, pp. 146-163.
Elsevier DOI 1702
Co-embedding generation BibRef

Yu, J.[Jun], Tao, D.C.[Da-Cheng], Rui, Y.[Yong], Cheng, J.[Jun],
Pairwise constraints based multiview features fusion for scene classification,
PR(46), No. 2, February 2013, pp. 483-496.
Elsevier DOI 1210
Scene classification, Fusion, Multiview dimensionality reduction, User labeling information Multiple features from different views. BibRef

Zhou, T., Tao, D.,
Double Shrinking Sparse Dimension Reduction,
IP(22), No. 1, January 2013, pp. 244-257.
IEEE DOI 1301
BibRef

Lai, Z.,
Sparse local discriminant projections for discriminant knowledge extraction and classification,
IET-CV(6), No. 6, 2012, pp. 551-559.
DOI Link 1301
BibRef

Cevikalp, H.[Hakan], Triggs, B.[Bill],
Hyperdisk based large margin classifier,
PR(46), No. 6, June 2013, pp. 1523-1531.
Elsevier DOI 1302
BibRef
Earlier:
Large margin classifiers based on convex class models,
Subspace09(101-108).
IEEE DOI 0910
Large margin classifier, Classification, Convex approximation; Hyperdisk, Kernel method; Support Vector Machine BibRef

Cevikalp, H.[Hakan], Triggs, B.[Bill],
Visual Object Detection Using Cascades of Binary and One-Class Classifiers,
IJCV(123), No. 3, July 2017, pp. 334-349.
Springer DOI 1706
BibRef
Earlier:
Efficient object detection using cascades of nearest convex model classifiers,
CVPR12(3138-3145).
IEEE DOI 1208
BibRef

Cevikalp, H.[Hakan], Saglamlar, H.[Halil],
Polyhedral Conic Classifiers for Computer Vision Applications and Open Set Recognition,
PAMI(43), No. 2, February 2021, pp. 608-622.
IEEE DOI 2101
Support vector machines, Training, Object detection, Visualization, Neural networks, Face, Dogs, Polyhedral conic classifiers, open set recognition BibRef

Cevikalp, H.[Hakan], Saglamlar, H.[Halil],
Transductive polyhedral conic classifiers for machine learning applications,
PRL(161), 2022, pp. 1-7.
Elsevier DOI 2209
Transductive learning, Polyhedral conic classifier, Large-margin classifier, Optimization BibRef

Cevikalp, H.[Hakan], Triggs, B.[Bill],
Polyhedral Conic Classifiers for Visual Object Detection and Classification,
CVPR17(4114-4122)
IEEE DOI 1711
Dogs, Object detection, Robustness, Support vector machines, Training, Visualization BibRef

Cevikalp, H.[Hakan],
Best Fitting Hyperplanes for Classification,
PAMI(39), No. 6, June 2017, pp. 1076-1088.
IEEE DOI 1705
BibRef
Earlier:
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

Cevikalp, H.[Hakan], Triggs, B.[Bill],
Face recognition based on image sets,
CVPR10(2567-2573).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Cevikalp, H.[Hakan], Triggs, B.[Bill], Jurie, F.[Frederic], Polikar, R.[Robi],
Margin-based discriminant dimensionality reduction for visual recognition,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Cevikalp, H.[Hakan], Yavuz, H.S.[Hasan Serhan],
Large Margin Classifier Based on Affine Hulls,
ICPR10(21-24).
IEEE DOI 1008
BibRef

Cevikalp, H.[Hakan],
Semi-supervised Distance Metric Learning by Quadratic Programming,
ICPR10(3352-3355).
IEEE DOI 1008
BibRef

Meng, D.Y.[De-Yu], Leung, Y.[Yee], Xu, Z.B.[Zong-Ben],
Passage method for nonlinear dimensionality reduction of data on multi-cluster manifolds,
PR(46), No. 8, August 2013, pp. 2175-2186.
Elsevier DOI 1304
Manifold learning; Multi-cluster manifolds; Nonlinear dimensionality reduction; Passage method BibRef

Kapoor, R., Gupta, R.,
Non-linear dimensionality reduction using fuzzy lattices,
IET-CV(7), No. 3, 2013, pp. -.
DOI Link 1307
BibRef

Kapoor, R., Gupta, R.,
Morphological mapping for non-linear dimensionality reduction,
IET-CV(9), No. 2, 2015, pp. 226-233.
DOI Link 1506
data visualisation BibRef

Gao, Q., Gao, F., Zhang, H., Hao, X.J., Wang, X.,
Two-Dimensional Maximum Local Variation Based on Image Euclidean Distance for Face Recognition,
IP(22), No. 10, 2013, pp. 3807-3817.
IEEE DOI 1309
Dimensionality reduction BibRef

Tao, D.C.[Da-Cheng], Jin, L., Yang, Z., Li, X.L.[Xue-Long],
Rank Preserving Sparse Learning for Kinect Based Scene Classification,
Cyber(43), No. 5, 2013, pp. 1406-1417.
IEEE DOI 1309
Dimension reduction classification. Using depth and low level features. BibRef

Gonen, M.,
Bayesian Supervised Dimensionality Reduction,
Cyber(43), No. 6, 2013, pp. 2179-2189.
IEEE DOI 1312
Bayes methods BibRef

Zhu, L.[Lin], Huang, D.S.[De-Shuang],
A Rayleigh-Ritz style method for large-scale discriminant analysis,
PR(47), No. 4, 2014, pp. 1698-1708.
Elsevier DOI 1402
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,
IP(23), No. 2, February 2014, pp. 920-930.
IEEE DOI 1402
data handling BibRef

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.Q.[Bei-Qi], Li, W.Y.[Wei-Yue],
UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification,
PandRS(89), No. 1, 2014, pp. 25-36.
Elsevier DOI 1403
Nonlinear dimensionality reduction BibRef

He, J.R.[Jin-Rong], Ding, L.X.[Li-Xin], Jiang, L.[Lei], Li, Z.K.[Zhao-Kui], Hu, Q.H.[Qing-Hui],
Intrinsic dimensionality estimation based on manifold assumption,
JVCIR(25), No. 5, 2014, pp. 740-747.
Elsevier DOI 1406
Intrinsic dimension estimation BibRef

Cui, Y.[Yan], Fan, L.[Liya],
A novel supervised dimensionality reduction algorithm: Graph-based Fisher analysis,
PR(45), No. 4, 2012, pp. 1471-1481.
Elsevier DOI 1410
Dimensionality reduction BibRef

Wong, W.K.,
Discover latent discriminant information for dimensionality reduction: Non-negative Sparseness Preserving Embedding,
PR(45), No. 4, 2012, pp. 1511-1523.
Elsevier DOI 1410
Sparse representation BibRef

Orsenigo, C.[Carlotta],
An improved set covering problem for Isomap supervised landmark selection,
PRL(49), No. 1, 2014, pp. 131-137.
Elsevier DOI 1410
Nonlinear dimensionality reduction BibRef

Wang, B.H.[Bing-Hui], Lin, C.[Chuang], Zhao, X.F.[Xue-Feng], Lu, Z.M.[Zhe-Ming],
Neighbourhood sensitive preserving embedding for pattern classification,
IET-IPR(8), No. 8, August 2014, pp. 489-497.
DOI Link 1410
face recognition BibRef

Pang, M.[Meng], Wang, B.H.[Bing-Hui], Fan, X.[Xin], Lin, C.[Chuang],
Discriminant Manifold Learning via Sparse Coding for Image Analysis,
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geophysical image processing BibRef

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feature extraction BibRef

Hong, Y.F.[Ying-Fu], Lee, S.[Sang_Bum], Oh, S.J.[Se-Jong],
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Dimensionality reduction BibRef

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PAMI(38), No. 7, July 2016, pp. 1452-1464.
IEEE DOI 1606
Approximation algorithms 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

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

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.
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image classification BibRef

Huang, K.K.[Ke-Kun], Dai, D.Q.[Dao-Qing], Ren, C.X.[Chuan-Xian],
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Dimensionality reduction BibRef

Casalino, G.[Gabriella], Gillis, N.[Nicolas],
Sequential dimensionality reduction for extracting localized features,
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Wang, Y.[Yong], Xie, J.B.[Jian-Bin], Wu, Y.[Yi],
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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.
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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.
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Hilbert spaces BibRef

Shao, G.W.[Guo-Wan], Sang, N.[Nong],
Regularized max-min linear discriminant analysis,
PR(66), No. 1, 2017, pp. 353-363.
Elsevier DOI 1704
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Fractional-step max-min distance analysis for dimension reduction,
ICPR12(396-400).
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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

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.
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Liu, H., Liu, L., Le, T.D., Lee, I., Sun, S., Li, J.,
Nonparametric Sparse Matrix Decomposition for Cross-View Dimensionality Reduction,
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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

Lai, Z., Xu, Y., Yang, J., Shen, L., Zhang, D.,
Rotational Invariant Dimensionality Reduction Algorithms,
Cyber(47), No. 11, November 2017, pp. 3733-3746.
IEEE DOI 1710
Feature extraction, Learning systems, Measurement, Principal component analysis, Robustness, Dimensionality reduction, image feature extraction, rotational, invariant, (RI), subspace, learning BibRef

Paul, R.[Rahul], Chalup, S.K.[Stephan K.],
A study on validating non-linear dimensionality reduction using persistent homology,
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Manifold learning BibRef

Ning, X., Li, W., Tang, B., He, H.,
BULDP: Biomimetic Uncorrelated Locality Discriminant Projection for Feature Extraction in Face Recognition,
IP(27), No. 5, May 2018, pp. 2575-2586.
IEEE DOI 1804
Dimensionality reduction, Face, Face recognition, Kernel, Linear programming, Manifolds, Robustness, uncorrelated space BibRef

Chen, S.B.[Si-Bao], Zuo, C.[Chong], Ding, C.[Chris], Luo, B.[Bin],
Non-greedy Max-min Large Margin based on L1-norm,
PRL(108), 2018, pp. 38-47.
Elsevier DOI 1805
Max-min, Large margin, L1-norm, Linear projection, Dimensionality reduction BibRef

López-Sánchez, D.[Daniel], Arrieta, A.G.[Angélica González], Corchado, J.M.[Juan M.],
Data-independent Random Projections from the feature-space of the homogeneous polynomial kernel,
PR(82), 2018, pp. 130-146.
Elsevier DOI 1806
Random Projection, Homogeneous polynomial kernel, Nonlinear dimensionality reduction BibRef

Wang, S.J.[Shu-Jian], Xie, D.[Deyan], Chen, F.[Fang], Gao, Q.X.[Quan-Xue],
Dimensionality reduction by LPP-L21,
IET-CV(12), No. 5, August 2018, pp. 659-665.
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Zhang, H.[Han], Nie, F.P.[Fei-Ping], Zhang, R.[Rui], Li, X.L.[Xue-Long],
Auto-weighted 2-dimensional maximum margin criterion,
PR(83), 2018, pp. 220-229.
Elsevier DOI 1808
Supervised learning, Auto-weighted parameter, 2-dimensional criterion, Dimensionality selection, Classification BibRef

Wu, S.H.[Shi-Hao], Bertholet, P.[Peter], Huang, H.[Hui], Cohen-Or, D.[Daniel], Gong, M.L.[Ming-Lun], Zwicker, M.[Matthias],
Structure-Aware Data Consolidation,
PAMI(40), No. 10, October 2018, pp. 2529-2537.
IEEE DOI 1809
Pre-clustering. Related to mean-shift, except seeks density modes. Project onto lower dimensional structure. Manifolds, Noise measurement, Clustering algorithms, Standards, Noise reduction, Smoothing methods, Data consolidation, filtering, manifold denoising BibRef

Xie, L., Yin, M., Yin, X., Liu, Y., Yin, G.,
Low-Rank Sparse Preserving Projections for Dimensionality Reduction,
IP(27), No. 11, November 2018, pp. 5261-5274.
IEEE DOI 1809
data reduction, feature extraction, learning (artificial intelligence), matrix decomposition, image classification BibRef

Wang, G.A.[Gao-Ang], Hwang, J.N.[Jenq-Neng], Rose, C.[Craig], Wallace, F.[Farron],
Uncertainty-Based Active Learning via Sparse Modeling for Image Classification,
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IEEE DOI 1810
approximation theory, Gaussian processes, image classification, image representation, image sampling, CNN BibRef

Gajamannage, K.[Kelum], Paffenroth, R.[Randy], Bollt, E.M.[Erik M.],
A nonlinear dimensionality reduction framework using smooth geodesics,
PR(87), 2019, pp. 226-236.
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Manifold, Nonlinear dimensionality reduction, Smoothing spline, Geodesics, Noisy measurements BibRef

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Bounded manifold completion,
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Manifold, Low-rank matrix completion, Positive semi-definite, Truncated nuclear norm, Gramian BibRef

Shi, Y.[Yong], Lei, M.L.[Ming-Long], Yang, H.[Hong], Niu, L.F.[Ling-Feng],
Diffusion network embedding,
PR(88), 2019, pp. 518-531.
Elsevier DOI 1901
Network embedding, Cascades, Diffusion process, Network inference, Dimension reduction BibRef

Hoyos-Idrobo, A.[Andrés], Varoquaux, G.[Gaël], Kahn, J.[Jonas], Thirion, B.[Bertrand],
Recursive Nearest Agglomeration (ReNA): Fast Clustering for Approximation of Structured Signals,
PAMI(41), No. 3, March 2019, pp. 669-681.
IEEE DOI 1902
Dimensionality reduction, Approximation algorithms, Signal processing algorithms, Feature extraction, approximation BibRef

Luo, T., Hou, C., Nie, F., Yi, D.,
Dimension Reduction for Non-Gaussian Data by Adaptive Discriminative Analysis,
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Face recognition, Dimensionality reduction, Distributed databases, Convergence, Face, linear discriminant analysis (LDA) BibRef

Najafabadi, A.A.S.[Ali Asghar Sharifi], Azar, F.T.[Farah Torkamani],
Removing redundancy data with preserving the structure and visuality in a database,
SIViP(13), No. 4, June 2019, pp. 745-752.
Springer DOI 1906
Reduce the database size but keep the essential information. (Faces) BibRef

Ali, M.[Mohammed], Jones, M.W.[Mark W.], Xie, X.H.[Xiang-Hua], Williams, M.[Mark],
TimeCluster: dimension reduction applied to temporal data for visual analytics,
VC(35), No. 6-8, June 2018, pp. 1013-1026.
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Wu, W., Kwong, S., Hou, J., Jia, Y., Ip, H.H.S.,
Simultaneous Dimensionality Reduction and Classification via Dual Embedding Regularized Nonnegative Matrix Factorization,
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IEEE DOI 1907
data reduction, data structures, iterative methods, matrix decomposition, optimisation, pattern classification, classification BibRef

Garcia-Vega, S., Castellanos-Dominguez, G.,
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Elsevier DOI 1909
Sequential learning, Adaptive learning-rate, Kernel adaptive filters, Correntropy BibRef

Bai, C.Z.[Cheng-Zu], Zhang, R.[Ren], Xu, Z.S.[Ze-Shui], Cheng, R.[Rui], Jin, B.G.[Bao-Gang], Chen, J.[Jian],
L1-norm-based kernel entropy components,
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Kernel entropy component analysis, Density estimation, Dimensionality reduction, Feature extraction, L1-norm BibRef

Shen, X.J.[Xiang-Jun], Liu, S.X.[Si-Xing], Bao, B.K.[Bing-Kun], Pan, C.H.[Chun-Hong], Zha, Z.J.[Zheng-Jun], Fan, J.P.[Jian-Ping],
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Dimensionality reduction, Graph embedding, Subspace learning, Least-squares BibRef

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Dimensionality reduction, Distance correlation (dCor), Kernel methods, Classification, Regression BibRef

He, L.[Lulu], Ye, J.M.[Ji-Min], E, J.W.[Jian-Wei],
Robust L1-norm two-dimensional collaborative representation-based projection for dimensionality reduction,
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Elsevier DOI 1912
Collaborative representation-based projection (CRP), L1-2DCRP, L1-norm, Face recognition, Dimensionality reduction BibRef

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Near-Convex Archetypal Analysis,
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Signal processing algorithms, Optimization, Standards, Tuning, Hyperspectral imaging, Data models, Dimensionality reduction, optimization BibRef

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BibRef
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Dimensionality Reduction of Proportional Data Through Data Separation Using Dirichlet Distribution,
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Dimensionality reduction, Feature extraction BibRef

Zhao, C., Mao, X., Chen, M., Yu, C.,
Continuous Approximation Based Dimension-Reduced Estimation for Arbitrary Sampling,
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Estimation, Direction-of-arrival estimation, Manifolds, Arrays, group sparse BibRef

Long, T.H.[Tian-Hang], Sun, Y.F.[Yan-Feng], Gao, J.B.[Jun-Bin], Hu, Y.L.[Yong-Li], Yin, B.C.[Bao-Cai],
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Locality preserving projection, Euler representation, Dimensionality reduction BibRef

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Feature extraction, Hyperspectral imaging, Dimensionality reduction, Learning systems, neighborhood margin BibRef

Atienza, N.[Nieves], Gonzalez-Díaz, R.[Rocio], Soriano-Trigueros, M.[Manuel],
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Persistent homology, Persistent entropy, Stability, Dimensionality reduction BibRef

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Nonlinearity, Dimensionality reduction, Divisive hierarchical clustering, Manifold clustering BibRef

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Capped L_p-Norm LDA for Outliers Robust Dimension Reduction,
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IEEE DOI 2008
Robustness, Signal processing algorithms, Optimization, Dimensionality reduction, Training, Iterative algorithms, image classification BibRef

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Generalized low-rank approximation of matrices based on multiple transformation pairs,
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Machine learning, Matrix data classification, Kronecker product, Dimensionality reduction, SVD, GLRAM BibRef

Eftekhari, A.[Armin], Hauser, R.A.[Raphael A.], Grammenos, A.[Andreas],
MOSES: A Streaming Algorithm for Linear Dimensionality Reduction,
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Memory-limited Online Subspace Estimation Scheme. Dimensionality reduction, Estimation, Optimization, Approximation algorithms, Principal component analysis, Ear, non-convex optimisation BibRef

Zhang, S., Ma, Z., Gan, W.,
Dimensionality Reduction for Tensor Data Based on Local Decision Margin Maximization,
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IEEE DOI 2011
Tensors, Optimization, Principal component analysis, Manifolds, Data mining, Dimensionality reduction, tensor data, supervised, local decision margin BibRef

Hu, H., Feng, D., Yang, F.,
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Collaborative representation, discriminant projection, nonlinear dimensionality reduction, small sample size BibRef

Gao, Y.L.[Yun-Long], Zhong, S.X.[Shu-Xin], Hu, K.L.[Kang-Li], Pan, J.Y.[Jin-Yan],
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Wang, Z.[Zheng], Nie, F.P.[Fei-Ping], Wang, R.[Rong], Yang, H.[Hui], Li, X.L.[Xue-Long],
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Supervised dimensionality reduction, Local structured feature learning, Dynamic maximum entropy graph BibRef

Zhao, Y.P., Chen, L., Chen, C.L.P.,
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Sparse matrices, Laplace equations, Manifolds, Dimensionality reduction, Encoding, Task analysis, ADMM BibRef

Nie, F.P.[Fei-Ping], Wang, Z.[Zheng], Wang, R.[Rong], Wang, Z.[Zhen], Li, X.L.[Xue-Long],
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Optimization, Robustness, Iterative algorithms, Dimensionality reduction, Principal component analysis, outlier BibRef

Wang, Z.[Zheng], Nie, F.P.[Fei-Ping], Zhang, C.[Canyu], Wang, R.[Rong], Li, X.L.[Xue-Long],
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Lee, J.[Jongmin], Kim, J.H.[Jang-Hyun], Oh, H.S.[Hee-Seok],
Spherical Principal Curves,
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IEEE DOI 2106
Manifolds, Dimensionality reduction, Data analysis, Surface treatment, Analytical models, Data models, Shape, spherical domain BibRef

Niu, G.[Guo], Ma, Z.M.[Zheng-Ming],
Tensor dimensionality reduction via mode product and HSIC,
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Lai, Z.H.[Zhi-Hui], Yu, Z.[Zhuozhen], Kong, H.[Heng], Shen, L.L.[Lin-Lin],
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Ridge regression, Robust dimensionality reduction, Two-dimensional jointly sparse projection, Robust discriminant regression (RDR) BibRef

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Mixture factor analysis with distance metric constraint for dimensionality reduction,
PR(121), 2022, pp. 108156.
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Dimensionality reduction, Mixture factor analysis, Distance metric constraint, Classification BibRef

Islam, M.T.[Md Tauhidul], Xing, L.[Lei],
Geometry and statistics-preserving manifold embedding for nonlinear dimensionality reduction,
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Manifold embedding, Dimensionality reduction, Geometry preservation, Nonlinear mapping BibRef

Kay, S.[Steven],
Dimensionality Reduction for Signal Detection,
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IEEE DOI 2202
Probability density function, Mean square error methods, Gaussian noise, Dimensionality reduction, Bayes methods, Standards, inference algorithms BibRef

Zhou, R.X.[Rui-Xu], Gao, W.S.[Wen-Sheng], Ding, D.W.[Deng-Wei], Liu, W.D.[Wei-Dong],
Supervised dimensionality reduction technology of generalized discriminant component analysis and its kernelization forms,
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Dimensionality reduction, Subspace projection, Generalized discriminant component analysis BibRef

Xing, S.S.[Samuel S.], Islam, M.T.[Md Tauhidul],
Utilizing differential characteristics of high dimensional data as a mechanism for dimensionality reduction,
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Elsevier DOI 2205
Reference data, Differential characteristics, Manifold embedding, Dimensionality reduction, Comparative analysis BibRef

Nie, F.P.[Fei-Ping], Zhao, X.W.[Xiao-Wei], Wang, R.[Rong], Li, X.L.[Xue-Long],
Fast Locality Discriminant Analysis With Adaptive Manifold Embedding,
PAMI(44), No. 12, December 2022, pp. 9315-9330.
IEEE DOI 2212
Dimensionality reduction, Principal component analysis, Feature extraction, Manifolds, Null space, Covariance matrices, Manifold structure of data BibRef

Tan, C.[Chao], Chen, S.[Sheng], Geng, X.[Xin], Ji, G.[Genlin],
A label distribution manifold learning algorithm,
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Elsevier DOI 2212
Multi-label learning, Label distribution learning, Manifold learning, Dimension reduction, Linear regression BibRef

Lu, Q.[Qin], Karanikolas, G.V.[Georgios V.], Giannakis, G.B.[Georgios B.],
Incremental Ensemble Gaussian Processes,
PAMI(45), No. 2, February 2023, pp. 1876-1893.
IEEE DOI 2301
Kernel, Radio frequency, Dimensionality reduction, Scalability, Training, Task analysis, Benchmark testing, Gaussian processes, regret analysis BibRef

Yan, W.Z.[Wen-Zhu], Yang, M.[Ming], Li, Y.[Yanmeng],
Robust Low Rank and Sparse Representation for Multiple Kernel Dimensionality Reduction,
CirSysVideo(33), No. 1, January 2023, pp. 1-15.
IEEE DOI 2301
Kernel, Feature extraction, Dimensionality reduction, Optimization, Sparse matrices, Task analysis, Support vector machines, 1 norm BibRef

Li, T.[Tao], Tan, L.[Lei], Huang, Z.[Zhehao], Tao, Q.H.[Qing-Hua], Liu, Y.P.[Yi-Peng], Huang, X.L.[Xiao-Lin],
Low Dimensional Trajectory Hypothesis is True: DNNs Can Be Trained in Tiny Subspaces,
PAMI(45), No. 3, March 2023, pp. 3411-3420.
IEEE DOI 2302
Training, Trajectory, Neural networks, Robustness, Dimensionality reduction, Visualization, Optimization methods, subspace BibRef

Qiu, H.Q.[Hai-Quan], Yang, Y.[Youlong], Pan, H.[Hua],
Underestimation modification for intrinsic dimension estimation,
PR(140), 2023, pp. 109580.
Elsevier DOI 2305
Intrinsic dimension, Parameter selection, Estimation method, Underestimation modification, Smooth manifold BibRef

Wang, X.[Xiang], Zhu, J.X.[Jun-Xing], Xu, Z.C.[Zi-Chen], Ren, K.J.[Kai-Jun], Liu, X.W.[Xin-Wang], Wang, F.Y.[Feng-Yun],
Local nonlinear dimensionality reduction via preserving the geometric structure of data,
PR(143), 2023, pp. 109663.
Elsevier DOI 2310
Dimensionality reduction, Embedding learning, Geometric preservation, Random walk BibRef

Tezekbayev, M.[Maxat],
Autoencoders for a manifold learning problem with a Jacobian rank constraint,
PR(143), 2023, pp. 109777.
Elsevier DOI 2310
Manifold learning, Dimensionality reduction, Alternating algorithm, Ky fan antinorm, Autoencoders, Rank constraints BibRef

Nellas, I.A.[Ioannis A.], Tasoulis, S.K.[Sotiris K.], Georgakopoulos, S.V.[Spiros V.], Plagianakos, V.P.[Vassilis P.],
Two phase cooperative learning for supervised dimensionality reduction,
PR(144), 2023, pp. 109871.
Elsevier DOI 2310
Artificial neural networks, Deep learning, Dimensionality reduction, Autoencoders, Image classification BibRef

Pal, S.[Soumyasundar], Valkanas, A.[Antonios], Coates, M.[Mark],
Population Monte Carlo With Normalizing Flow,
SPLetters(31), 2024, pp. 16-20.
IEEE DOI 2401
Alternative to Markov Chain Monte Carlo. BibRef

Lai, Z.H.[Zhi-Hui], Chen, F.[Foping], Wen, J.J.[Jia-Jun],
Multi-view robust regression for feature extraction,
PR(149), 2024, pp. 110219.
Elsevier DOI 2403
Image classification, Small-class problem, Linear regression (LR) BibRef

Bui, A.T.[Anh Tuan],
Dimension Reduction With Prior Information for Knowledge Discovery,
PAMI(46), No. 5, May 2024, pp. 3625-3636.
IEEE DOI 2404
Dimensionality reduction, Manifolds, Measurement, Knowledge discovery, Task analysis, Principal component analysis, SMACOF BibRef

Wang, J.Y.[Jing-Yu], Yin, H.[Hengheng], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
Adaptive and fuzzy locality discriminant analysis for dimensionality reduction,
PR(151), 2024, pp. 110382.
Elsevier DOI 2404
Adaptive and fuzzy k-means, Discrete fuzzy membership, Subblock partition, Locality discriminant analysis BibRef

Leygonie, R.[Rebecca], Lobry, S.[Sylvain], Vimont, G.[Guillaume], Wendling, L.[Laurent],
Transforming Multidimensional Data into Images to Overcome the Curse of Dimensionality,
ICIP23(700-704)
IEEE DOI 2312
BibRef

Gilet, C.[Cyprien], Deprez, M.[Marie], Barbry, P.[Pacal], Caillau, J.B.[Jean-Baptiste], Barlaud, M.[Michel],
Efficient Clustering Using Alternating Minimization And A Projection-Gradient Method For Dimension Reduction,
ICIP22(176-180)
IEEE DOI 2211
Dimensionality reduction, Sequential analysis, Costs, RNA, Minimization, Iterative algorithms BibRef

Guo, Y.H.[Yun-Hui], Wang, X.D.[Xu-Dong], Chen, Y.[Yubei], Yu, S.X.[Stella X.],
Clipped Hyperbolic Classifiers Are Super-Hyperbolic Classifiers,
CVPR22(1-10)
IEEE DOI 2210
Training, Representation learning, Neural networks, Semantics, Benchmark testing, Feature extraction, Machine learning, Representation learning BibRef

Guo, Y.H.[Yun-Hui], Guo, H.R.[Hao-Ran], Yu, S.X.[Stella X.],
CO-SNE: Dimensionality Reduction and Visualization for Hyperbolic Data,
CVPR22(11-20)
IEEE DOI 2210
Representation learning, Dimensionality reduction, Semantics, Data visualization, Gaussian distribution, Nonhomogeneous media, Representation learning BibRef

Sarfraz, M.S.[M. Saquib], Koulakis, M.[Marios], Seibold, C.[Constantin], Stiefelhagen, R.[Rainer],
Hierarchical Nearest Neighbor Graph Embedding for Efficient Dimensionality Reduction,
CVPR22(336-345)
IEEE DOI 2210
Dimensionality reduction, Measurement, Codes, Data visualization, Proposals, Machine learning, grouping and shape analysis BibRef

Fan, X.[Xiran], Yang, C.H.[Chun-Hao], Vemuri, B.C.[Baba C.],
Nested Hyperbolic Spaces for Dimensionality Reduction and Hyperbolic NN Design,
CVPR22(356-365)
IEEE DOI 2210
Dimensionality reduction, Manifolds, Deep learning, Extraterrestrial measurements, Deep learning architectures and techniques BibRef

Litany, O.[Or], Morcos, A.[Ari], Sridhar, S.[Srinath], Guibas, L.J.[Leonidas J.], Hoffman, J.[Judy],
Representation Learning Through Latent Canonicalizations,
WACV21(645-654)
IEEE DOI 2106
Training, Dimensionality reduction, Atmospheric measurements, Linearity, Focusing BibRef

Jordão, A.[Artur], Lie, M.[Maiko], Cunha de Melo, V.H.[Victor Hugo], Schwartz, W.R.[William Robson],
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method,
WACV21(1420-1428)
IEEE DOI 2106
Dimensionality reduction, Streaming media, Feature extraction, Computational efficiency, Task analysis, Covariance matrices BibRef

Sheikhi, G.[Ghazaal], Altnçay, H.[Hakan],
Supervised Feature Embedding for Classification by Learning Rank-based Neighborhoods,
ICPR21(9340-9347)
IEEE DOI 2105
Dimensionality reduction, Neural networks, Encoding, embedding, representative learning, hot vectors BibRef

Becker, M.[Martin], Lippel, J.[Jens], Zielke, T.[Thomas],
Dimensionality Reduction for Data Visualization and Linear Classification, and the Trade-off between Robustness and Classification Accuracy,
ICPR21(6478-6485)
IEEE DOI 2105
Dimensionality reduction, Neural networks, Data visualization, Robustness, Linear discriminant analysis, Decoding BibRef

Jiang, B., Shen, M.,
Dimensionality Reduction Via Diffusion Map Improved With Supervised Linear Projection,
ICIP20(1796-1800)
IEEE DOI 2011
Dimensionality reduction, Feature extraction, Linear programming, Manifolds, Kernel, Eigenvalues and eigenfunctions, supervised learning BibRef

Allaoui, M.[Mebarka], Kherfi, M.L.[Mohammed Lamine], Cheriet, A.[Abdelhakim],
Considerably Improving Clustering Algorithms Using Umap Dimensionality Reduction Technique: A Comparative Study,
ICISP20(317-325).
Springer DOI 2009
BibRef

Kachan, O.,
Persistent Homology-based Projection Pursuit,
Diff-CVML20(3744-3751)
IEEE DOI 2008
Topology, Optimization, Manifolds, Dimensionality reduction, Loss measurement, Clustering algorithms BibRef

Gong, S.[Sixue], Boddeti, V.N.[Vishnu Naresh], Jain, A.K.[Anil K.],
On the Intrinsic Dimensionality of Image Representations,
CVPR19(3982-3991).
IEEE DOI 2002
BibRef

Zhang, Y.S.[You-Shan], Xing, J.R.[Jia-Rui], Zhang, M.M.[Miao-Miao],
Mixture Probabilistic Principal Geodesic Analysis,
MFCA19(196-208).
Springer DOI 1912
BibRef

Zhang, J., Wang, J.,
Linear Discriminative Sparsity Preserving Projections for Dimensionality Reduction,
ICPR18(159-164)
IEEE DOI 1812
Sparse matrices, Manifolds, Dimensionality reduction, Linear programming, Learning systems, Image recognition, Image reconstruction BibRef

Luo, X., Durrant, R.J.,
Maximum Gradient Dimensionality Reduction,
ICPR18(501-506)
IEEE DOI 1812
Dimensionality reduction, Principal component analysis, Task analysis, Training data, Linear regression, Feature extraction BibRef

Chen, S., Lee, Y., Wang, J.,
Locality Preserving Discriminative Complex-Valued Latent Variable Model,
ICPR18(1169-1174)
IEEE DOI 1812
Data models, Linear programming, Principal component analysis, Dimensionality reduction, Kernel, Computational modeling BibRef

Liu, X.F.[Xiao-Feng], Li, Z.F.[Zhao-Feng], Kong, L.S.[Ling-Sheng], Diao, Z.H.[Zhi-Hui], Yan, J.L.[Jun-Liang], Zou, Y.[Yang], Yang, C.[Chao], Jia, P.[Ping], You, J.[Jane],
A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification,
ICPR18(1493-1498)
IEEE DOI 1812
Optimization, Collaboration, Training, Task analysis, Face recognition, Feature extraction, Dimensionality reduction, sparse representation BibRef

Zhang, H., Gabbouj, M.[Moncef],
Feature Dimensionality Reduction with Graph Embedding and Generalized Hamming Distance,
ICIP18(1083-1087)
IEEE DOI 1809
Dimensionality reduction, Principal component analysis, Hamming distance, Mutual information, Measurement, Dogs, multilabel BibRef

Li, Y.,
Locally preserving projection on symmetric positive definite matrix lie group,
ICIP17(1217-1221)
IEEE DOI 1803
Covariance matrices, Dimensionality reduction, Laplace equations, Manifolds, Measurement, Silicon, Symmetric matrices, SPD matrix Lie group BibRef

Sun, Z.H., Hoogs, A.,
Compact image representation by binary component analysis,
ICIP17(2771-2775)
IEEE DOI 1803
Correlation, Dimensionality reduction, Face, Image representation, Principal component analysis, Quantization (signal), Uncertainty BibRef

Kloss, R.B.[Ricardo Barbosa], Jordão, A.[Artur], Schwartz, W.R.[William Robson],
Boosted Projection: An Ensemble of Transformation Models,
CIARP17(331-338).
Springer DOI 1802
BibRef

Griparis, A.[Andreea], Faur, D.[Daniela], Datcu, M.[Mihai],
Evaluation of Dimensionality Reduction Methods for Remote Sensing Images Using Classification and 3D Visualization,
ACIVS17(203-211).
Springer DOI 1712
BibRef

Mehta, A.[Aditya], Sekhar, C.C.[C. Chandra],
Kernel Entropy Discriminant Analysis for Dimension Reduction,
PReMI17(35-42).
Springer DOI 1711
BibRef

Yoshiyasu, Y., Yoshida, E.,
Nonlinear dimensionality reduction by curvature minimization,
ICPR16(3590-3596)
IEEE DOI 1705
Distortion, Laplace equations, Manifolds, Minimization, Optimization, 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

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.P.[Pei-Pei], 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
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Honko, P.[Piotr],
Scalability of Data Decomposition Based Algorithms: Attribute Reduction Problem,
PReMI15(387-396).
Springer DOI 1511
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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
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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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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.Q.[Si-Qi], Ji, Q.A.[Qi-Ang],
Feature Learning Using Bayesian Linear Regression Model,
ICPR14(1502-1507)
IEEE DOI 1412
Accuracy BibRef

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

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).
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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).
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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).
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Liao, W.H.[Wen-Hung],
Commensurate dimensionality reduction for extended local ternary patterns,
ICPR12(3013-3016).
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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).
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Hariharan, B.[Bharath], Malik, J.[Jitendra], Ramanan, D.[Deva],
Discriminative Decorrelation for Clustering and Classification,
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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,
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Hong, Y.[Yi], Li, Q.N.[Quan-Nan], Jiang, J.Y.[Jia-Yan], Tu, Z.W.[Zhuo-Wen],
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ICCV11(906-913).
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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,
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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).
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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).
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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).
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Sáenz, C.[Carlos], Hernández, B.[Begoña], Alberdi, C.[Coro], Alfonso, S.[Santiago], Diñeiro, J.M.[José Manuel],
The Number of Linearly Independent Vectors in Spectral Databases,
SCIA09(570-579).
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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).
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Jeong, S.D.[Seung-Do], Kim, S.W.[Sang-Wook], Kim, W.Y.[Whoi-Yul], Choi, B.U.[Byung-Uk],
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Regression with interval output values,
ICPR08(1-4).
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Jia, Y.Q.[Yang-Qing], Zhang, C.S.[Chang-Shui],
Local Regularized Least-Square Dimensionality Reduction,
ICPR08(1-4).
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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).
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Supervised Isomap with Dissimilarity Measures in Embedding Learning,
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Li, J.[Jun], Hao, P.W.[Peng-Wei],
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Dimensionality Reduction with Adaptive Kernels,
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ICPR06(II: 447-450).
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Valev, V.,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Semi-Supervised, Unsupervised Dimensionality Reduction .

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