14.1.3.1 Sparse Feature Selection

Yuan, Y.H.[Yun-Hao], Sun, Q.S.[Quan-Sen], Zhou, Q.A.[Qi-Ang], Xia, D.S.[De-Shen],
A novel multiset integrated canonical correlation analysis framework and its application in feature fusion,
PR(44), No. 5, May 2011, pp. 1031-1040.
Elsevier DOI 1101
Pattern recognition, Canonical correlation analysis, Feature extraction, Multiset canonical correlation analysis, Feature fusion BibRef

Ji, H.K.[Hong-Kun], Shen, X.B.[Xiao-Bo], Sun, Q.S.[Quan-Sen], Ji, Z.X.[Ze-Xuan],
Sparse Discrimination Based Multiset Canonical Correlation Analysis for Multi-Feature Fusion and Recognition,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Yan, H.[Hui], Yuan, X.T.[Xiao-Tong], Yan, S.C.[Shui-Cheng], Yang, J.Y.[Jing-Yu],
Correntropy based feature selection using binary projection,
PR(44), No. 12, December 2011, pp. 2834-2842.
Elsevier DOI 1107
Feature selection, Mutual information, Correntropy, Binary projection matrix BibRef

Yan, H.[Hui], Yang, J.[Jian],
Sparse discriminative feature selection,
PR(48), No. 5, 2015, pp. 1827-1835.
Elsevier DOI 1502
Joint feature selection BibRef

Yan, H.[Hui], Yang, W.K.[Wan-Kou], Yang, J.[Jian], Yang, J.Y.[Jing-Yu],
Discriminant feature extraction based on center distance,
ICIP09(1249-1252).
IEEE DOI 0911
BibRef

Yan, H.[Hui], Yang, J.[Jian],
Joint Laplacian feature weights learning,
PR(47), No. 3, 2014, pp. 1425-1432.
Elsevier DOI 1312
Feature selection BibRef

Yan, H.[Hui], Jin, Z.[Zhong], Yang, J.[Jian],
Sparse Representation Preserving for Unsupervised Feature Selection,
ICPR14(1574-1578)
IEEE DOI 1412
Face BibRef

Zheng, W.M.[Wen-Ming], Lin, Z.C.[Zhou-Chen],
A new discriminant subspace analysis approach for multi-class problems,
PR(45), No. 4, April 2012, pp. 1426-1435.
Elsevier DOI 1112
Fukunaga-Koontz Transform, Common principal component analysis; Feature extraction BibRef

Yan, J.J.[Jing-Jie], Zheng, W.M.[Wen-Ming], Zhou, X.Y.[Xiao-Yan], Zhao, Z.J.[Zhi-Jian],
Sparse 2-D Canonical Correlation Analysis via Low Rank Matrix Approximation for Feature Extraction,
SPLetters(19), No. 1, January 2012, pp. 51-54.
IEEE DOI 1112
BibRef

Shi, C.J.[Cai-Juan], Ruan, Q.Q.[Qiu-Qi], An, G.Y.[Gao-Yun], Zhao, R.Z.[Rui-Zhen],
Hessian Semi-Supervised Sparse Feature Selection Based on L_2,1/2 -Matrix Norm,
MultMed(17), No. 1, January 2015, pp. 16-28.
IEEE DOI 1502
Hessian matrices BibRef

Shi, C.J.[Cai-Juan], Ruan, Q.Q.[Qiu-Qi], An, G.Y.[Gao-Yun], Ge, C.[Chao],
Semi-supervised sparse feature selection based on multi-view Laplacian regularization,
IVC(41), No. 1, 2015, pp. 1-10.
Elsevier DOI 1508
Multi-view learning BibRef

Shi, C.J.[Cai-Juan], An, G.Y.[Gao-Yun], Zhao, R.Z.[Rui-Zhen], Ruan, Q.Q.[Qiu-Qi], Tian, Q.[Q1],
Multiview Hessian Semisupervised Sparse Feature Selection for Multimedia Analysis,
CirSysVideo(27), No. 9, September 2017, pp. 1947-1961.
IEEE DOI 1709
Algorithm design and analysis, Geometry, Information science, Kernel, Laplace equations, Manifolds, Semisupervised learning, 3D motion analysis, Hessian regularization, image annotation, multiview learning, sparse feature selection, video, concept, detection BibRef

Zhang, L.F.[Le-Fei], Zhang, Q.[Qian], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng], Huang, X.[Xin], Du, B.[Bo],
Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding,
PR(48), No. 10, 2015, pp. 3102-3112.
Elsevier DOI 1507
Multiview BibRef

Hou, C., Jiao, Y., Nie, F., Luo, T., Zhou, Z.H.,
2D Feature Selection by Sparse Matrix Regression,
IP(26), No. 9, September 2017, pp. 4255-4268.
IEEE DOI 1708
convergence, feature selection, image classification, optimisation, regression analysis, sparse matrices, 2D feature selection, 2D matrix data, SMR, data points, effective optimization method, image processing, matrix elements, provable convergence behavior, regression coefficients, scene classification, sparse constraints, sparse matrix regression, vector-based approaches, Algorithm design and analysis, Feature extraction, Matrix converters, Radio frequency, Robustness, Sparse matrices, Training, Two dimensional data, feature selection, scene classification, sparse matrix regression BibRef

Foroughi, H.[Homa], Ray, N.[Nilanjan], Zhang, H.[Hong],
Object Classification With Joint Projection and Low-Rank Dictionary Learning,
IP(27), No. 2, February 2018, pp. 806-821.
IEEE DOI 1712
Dictionaries, Lighting, Noise measurement, Training, Joint projection and dictionary learning, sparse representation BibRef

Foroughi, H.[Homa], Shakeri, M.[Moein], Ray, N.[Nilanjan], Zhang, H.[Hong],
Face Recognition Using Multi-Modal Low-Rank Dictionary Learning,
ICIP17(1082-1086)
IEEE DOI 1803
BibRef
Earlier:
Joint Feature Selection with Low-rank Dictionary Learning,
BMVC15(xx-yy).
DOI Link 1601
Dictionaries, Face, Face recognition, Feature extraction, Lighting, Machine learning, Robustness, Face recognition, Multi-modal dictionary learning BibRef

Huang, X.J.[Xiao-Juan], Zhang, L.[Li], Wang, B.J.[Bang-Jun], Zhang, Z.[Zhao], Li, F.Z.[Fan-Zhang],
Feature weight estimation based on dynamic representation and neighbor sparse reconstruction,
PR(81), 2018, pp. 388-403.
Elsevier DOI 1806
Feature weighting, Feature selection, Relief, Sparse learning, Local hyperplane, regularization, Classification BibRef

Zhu, Y.H.[Yong-Hua], Zhang, X.J.[Xue-Jun], Hu, R.Y.[Rong-Yao], Wen, G.Q.[Guo-Qiu],
Adaptive structure learning for low-rank supervised feature selection,
PRL(109), 2018, pp. 89-96.
Elsevier DOI 1806
Adaptive structure learning, Sparsity representation, Local structure preservation BibRef

Yi, Y.[Yugen], Zhou, W.[Wei], Liu, Q.H.[Qing-Hua], Luo, G.L.[Guo-Liang], Wang, J.Z.[Jian-Zhong], Fang, Y.M.[Yu-Ming], Zheng, C.X.[Cai-Xia],
Ordinal preserving matrix factorization for unsupervised feature selection,
SP:IC(67), 2018, pp. 118-131.
Elsevier DOI 1808
Unsupervised feature selection, Matrix factorization, Ordinal locality structure preserving, Sparsity and low redundancy BibRef

Chen, X.H.[Xiu-Hong], Lu, Y.[Yun],
Robust graph regularised sparse matrix regression for two-dimensional supervised feature selection,
IET-IPR(14), No. 9, 20 July 2020, pp. 1740-1749.
DOI Link 2007
BibRef

Lu, Y.[Yun], Chen, X.H.[Xiu-Hong],
Joint Feature Weighting and Adaptive Graph-Based Matrix Regression for Image Supervised Feature Selection,
SP:IC(90), 2021, pp. 116044.
Elsevier DOI 2012
Matrix regression, Feature selection, Feature weight matrix, Graph matrix, Classification BibRef

Chen, X.H.[Xiu-Hong], Lu, Y.[Yun], Zhang, J.[Jun], Zhu, X.Y.[Xing-Yu],
Margin-based discriminant embedding guided sparse matrix regression for image supervised feature selection,
CVIU(212), 2021, pp. 103273.
Elsevier DOI 2110
Two dimensional image, Supervised feature selection, Sparse matrix regression, Margin, Discriminant embedding, Classification BibRef

Zhu, X.Y.[Xing-Yu], Chen, X.H.[Xiu-Hong],
Low-rank nonnegative sparse representation and local preservation-based matrix regression for supervised image feature selection,
IET-IPR(15), No. 13, 2021, pp. 3021-3036.
DOI Link 2110
BibRef

Chen, X.H.[Xiu-Hong], Zhu, X.Y.[Xing-Yu], Lu, Y.[Yun], Pu, Z.F.[Zhi-Fang],
Non-negative low-rank adaptive preserving sparse matrix regression model for supervised image feature selection and classification,
IET-IPR(17), No. 7, 2023, pp. 2056-2071.
DOI Link 2305
adaptive graph matrix, classification, feature selection, low-rank representation, non-negative constraint BibRef

Zhou, J.T.Y.[Joey Tian-Yi],
Feature Selection With Multi-Source Transfer,
CirSysVideo(32), No. 5, May 2022, pp. 2638-2646.
IEEE DOI 2205
Feature extraction, Support vector machines, Training, Training data, Linear programming, sparsity optimization BibRef

Shang, R.H.[Rong-Hua], Kong, J.R.[Jia-Rui], Zhang, W.T.[Wei-Tong], Feng, J.[Jie], Jiao, L.C.[Li-Cheng], Stolkin, R.[Rustam],
Uncorrelated feature selection via sparse latent representation and extended OLSDA,
PR(132), 2022, pp. 108966.
Elsevier DOI 2209
Unsupervised feature selection, Sparse latent representation, OLSDA, Pseudo-labels, Uncorrelated constraints BibRef

Kong, J.R.[Jia-Rui], Shang, R.H.[Rong-Hua], Zhang, W.T.[Wei-Tong], Wang, C.[Chao], Xu, S.[Songhua],
Robust feature selection via central point link information and sparse latent representation,
PR(154), 2024, pp. 110617.
Elsevier DOI 2406
Center matrix, Link information, Sparse latent representation, Unsupervised feature selection, Dual graph structure BibRef

Wang, J.Y.[Jing-Yu], Wang, H.M.[Hong-Mei], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
Sparse feature selection via fast embedding spectral analysis,
PR(139), 2023, pp. 109472.
Elsevier DOI 2304
Unsupervised learning, Feature selection, Spectral analysis, Sparse subspace, -Norm BibRef

Nie, F.P.[Fei-Ping], Xie, F.Y.[Fang-Yuan], Yu, W.Z.[Wei-Zhong], Li, X.L.[Xue-Long],
Parameter-Insensitive Min Cut Clustering With Flexible Size Constrains,
PAMI(46), No. 8, August 2024, pp. 5479-5492.
IEEE DOI 2407
Clustering algorithms, Optimization, Clustering methods, NP-hard problem, Vectors, Task analysis, Symmetric matrices, min cut BibRef

Li, G.Q.[Guo-Quan], Yang, L.X.[Lin-Xi], Zhao, K.Q.[Ke-Quan],
A unified model for the sparse optimal scoring problem,
PR(133), 2023, pp. 108976.
Elsevier DOI 2210
Optimal scoring, Linear discriminant analysis, Feature selection, norm, Sparseness BibRef

Jiang, B.[Bo], Wang, B.B.[Bei-Bei], Luo, B.[Bin],
Sparse norm regularized attribute selection for graph neural networks,
PR(137), 2023, pp. 109265.
Elsevier DOI 2302
Graph neural networks, Feature selection, Sparse regularization, Semi-supervised learning BibRef

Zhou, P.[Peng], Chen, J.Y.[Jiang-Yong], Du, L.[Liang], Li, X.J.[Xue-Jun],
Balanced Spectral Feature Selection,
Cyber(53), No. 7, July 2023, pp. 4232-4244.
IEEE DOI 2307
Feature extraction, Periodic structures, Unsupervised learning, Time complexity, Task analysis, Sparse matrices, unsupervised learning BibRef

Qian, W.B.[Wen-Bin], Liu, J.[Jiale], Yang, W.J.[Wen-Ji], Huang, J.T.[Jin-Tao], Ding, W.P.[Wei-Ping],
Partial label feature selection based on noisy manifold and label distribution,
PR(156), 2024, pp. 110791.
Elsevier DOI 2408
Feature selection, Label distribution, Manifold learning, Feature dependency, Partial label learning BibRef

Sheikhpour, R.[Razieh], Berahmand, K.[Kamal], Mohammadi, M.[Mehrnoush], Khosravi, H.[Hassan],
Sparse feature selection using hypergraph Laplacian-based semi-supervised discriminant analysis,
PR(157), 2025, pp. 110882.
Elsevier DOI 2409
Semi-supervised feature selection, Semi-supervised discriminant analysis, Hypergraph-Laplacian, Sparse models BibRef

Yang, H.C.[Hai-Chuan], Huang, Y.J.[Yi-Jun], Tran, L.[Lam], Liu, J.[Ji], Huang, S.[Shuai],
On Benefits of Selection Diversity via Bilevel Exclusive Sparsity,
CVPR16(5945-5954)
IEEE DOI 1612
BibRef

Liu, B.Y.[Bing-Yuan], Liu, J.[Jing], Bai, X.[Xiao], Lu, H.Q.[Han-Qing],
Regularized Hierarchical Feature Learning with Non-negative Sparsity and Selectivity for Image Classification,
ICPR14(4293-4298)
IEEE DOI 1412
Biological system modeling BibRef

Liu, M.X.[Ming-Xia], Sun, D.[Dan], Zhang, D.I.[Daoq-Iang],
Sparsity Score: A new filter feature selection method based on graph,
ICPR12(959-962).
WWW Link. 1302
BibRef

Yang, J.[Jian], Chu, D.[Delin],
Sparse Representation Classifier Steered Discriminative Projection,
ICPR10(694-697).
IEEE DOI 1008
BibRef

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