14.1.3.1 Sparse Feature Selection

Chapter Contents (Back)
Feature Selection. Classification. Pattern Recognition. A somewhat arbitrary subset.

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
computer vision, convergence, feature selection, image classification, optimisation, regression analysis, sparse matrices, 2D feature selection, 2D matrix data, SMR, computer vision, 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.[Caixia],
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

Huang, X.Y.[Xia-Yuan], Nie, X.L.[Xiang-Li],
Multi-View Feature Selection for PolSAR Image Classification via L_2,1 Sparsity Regularization and Manifold Regularization,
IP(30), 2021, pp. 8607-8618.
IEEE DOI 2110
Feature extraction, Manifolds, Correlation, Optimization, Synthetic aperture radar, Scattering, Matrix decomposition, manifold regularization BibRef


Dornaika, F., Khoder, A.,
Feature Extraction by Joint Robust Discriminant Analysis and Inter-class Sparsity,
ICPR21(2972-2979)
IEEE DOI 2105
BibRef
And: A2, A1:
Feature Extraction and Selection via Robust Discriminant Analysis and Class Sparsity,
ICPR21(7258-7264)
IEEE DOI 2105
Training, Feature extraction, Linear programming, Linear discriminant analysis, Task analysis, image classification. Feature extraction, Minimization, Linear programming, Linear discriminant analysis 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
Unsupervised Feature Selection .


Last update:Nov 30, 2021 at 22:19:38