14.1.3.3 Unsupervised Feature Selection

Lakshminarasimhan, A.L., and Dasarathy, B.V.,
A Unified Approach to Feature Selection and Learning in Unsupervised Environments,
TC(24), September 1975, pp. 948-952. BibRef 7509

Dasarathy, B.V.,
Feature Selection and Concept of Immediate Neighborhood in the Context of Clustering Techniques,
PIEEE(62), No. 4, April 1974, pp. 529-530. BibRef 7404

Dasarathy, B.V.,
FEAST: Feature Evaluation and Selection Technique for Deployment in Unsupervised Nonparametric Environments,
CIS(6), No. 4, September 1977, pp. 307-315. BibRef 7709

Dasarathy, B.V.,
AHIMSA: Ad hoc Histogram Information Measure Sensing Algorithm for Feature Selection in the Context of Histogram Inspired Clustering Techniques,
PIEEE(64), No. 9, September 1976, pp. 1446-1447. BibRef 7609

Dasarathy, B.V.,
A Generalized Discriminant Hyperplane Approach to Pattern Classification,
PRL(12), No. 2, February 1991, pp. 127-128. BibRef 9102

Basak, J.[Jayanta], De, R.K.[Rajat K.], Pal, S.K.[Sankar K.],
Unsupervised Feature Selection Using a Neuro-Fuzzy Approach,
PRL(19), No. 11, 30 September 1998, pp. 997-1006. BibRef 9809

Mitra, P.[Pabrita], Murthy, C.A., Pal, S.K.[Sankar K.],
Unsupervised Feature Selection Using Feature Similarity,
PAMI(24), No. 3, March 2002, pp. 301-312.
IEEE DOI 0202
BibRef
And: Correction: PAMI(24), No. 6, June 2002, pp. 721.
IEEE DOI 0206
Feature selection for large (dimension and size) data sets. BibRef

Li, Y.H.[Yuan-Hong], Dong, M.[Ming], Hua, J.[Jing],
Localized feature selection for clustering,
PRL(29), No. 1, 1 January 2008, pp. 10-18.
Elsevier DOI 0711
BibRef
And:
Feature selection for clustering with constraints using Jensen-Shannon divergence,
ICPR08(1-4).
IEEE DOI 0812
Clustering, Unsupervised learning, Feature selection, Scatter separability BibRef

Li, Y.H.[Yuan-Hong], Dong, M.[Ming], Hua, J.[Jing],
Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures,
PAMI(31), No. 5, May 2009, pp. 953-960.
IEEE DOI 0903
BibRef
Earlier:
Localized feature selection for Gaussian mixtures using variational learning,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Ferreira, A.J.[Artur J.], Figueiredo, M.A.T.[Mário A.T.],
An unsupervised approach to feature discretization and selection,
PR(45), No. 9, September 2012, pp. 3048-3060.
Elsevier DOI 1206
BibRef
Earlier:
Unsupervised Joint Feature Discretization and Selection,
IbPRIA11(200-207).
Springer DOI 1106
Feature discretization, Feature quantization, Feature selection; Linde-Buzo-Gray algorithm, Sparse data, Support vector machines, Naïve Bayes, k-nearest neighbor BibRef

Ferreira, A.J.[Artur J.], Figueiredo, M.A.T.[Mário A.T.],
Efficient feature selection filters for high-dimensional data,
PRL(33), No. 13, 1 October 2012, pp. 1794-1804.
Elsevier DOI 1208
Feature selection, Filters, Dispersion measures, Similarity measures; High-dimensional data BibRef

Ferreira, A.J.[Artur J.], Figueiredo, M.A.T.[Mário A.T.],
An Incremental Bit Allocation Strategy for Supervised Feature Discretization,
IbPRIA13(526-534).
Springer DOI 1307
BibRef

Mao, K.Z.,
Identifying critical variables of principal components for unsupervised feature selection,
SMC-B(35), No. 2, April 2005, pp. 339-344.
IEEE DOI 0508
BibRef

Cai, R.C.[Rui-Chu], Zhang, Z.J.[Zhen-Jie], Hao, Z.F.[Zhi-Feng],
BASSUM: A Bayesian semi-supervised method for classification feature selection,
PR(44), No. 4, April 2011, pp. 811-820.
Elsevier DOI 1101
Feature selection, Semi-supervised, Structured object, Markov blanket; Conditional independence test BibRef

Breaban, M.[Mihaela], Luchian, H.[Henri],
A unifying criterion for unsupervised clustering and feature selection,
PR(44), No. 4, April 2011, pp. 854-865.
Elsevier DOI 1101
Unsupervised feature selection, Unsupervised clustering, Global optimization BibRef

Kalakech, M.[Mariam], Biela, P.[Philippe], Macaire, L.[Ludovic], Hamad, D.[Denis],
Constraint scores for semi-supervised feature selection: A comparative study,
PRL(32), No. 5, 1 April 2011, pp. 656-665.
Elsevier DOI 1103
Feature selection, Pairwise constraints, Kendall's coefficient; Constraint scores, Laplacian score, Fisher score BibRef

Qian, Y.H.[Yu-Hua], Liang, J.[Jiye], Pedrycz, W.[Witold], Dang, C.Y.[Chuang-Yin],
An efficient accelerator for attribute reduction from incomplete data in rough set framework,
PR(44), No. 8, August 2011, pp. 1658-1670.
Elsevier DOI 1104
Feature selection, Rough set theory, Incomplete information systems; Positive approximation, Granular computing BibRef

Wang, S.P.[Shi-Ping], Pedrycz, W.[Witold], Zhu, Q.X.[Qing-Xin], Zhu, W.[William],
Subspace learning for unsupervised feature selection via matrix factorization,
PR(48), No. 1, 2015, pp. 10-19.
Elsevier DOI 1410
Machine learning BibRef

Zhou, N.[Nan], Xu, Y.Y.[Yang-Yang], Cheng, H.[Hong], Fang, J.[Jun], Pedrycz, W.[Witold],
Global and local structure preserving sparse subspace learning: An iterative approach to unsupervised feature selection,
PR(53), No. 1, 2016, pp. 87-101.
Elsevier DOI 1602
Machine learning BibRef

Zhou, N.[Nan], Cheng, H.[Hong], Zheng, Y.L., He, L.T., Pedrycz, W.[Witold],
Unsupervised feature selection by nonnegative sparsity adaptive subspace learning,
ICWAPR16(18-24)
IEEE DOI 1611
Adaptation models BibRef

Zhou, N.[Nan], Xu, Y.Y.[Yang-Yang], Cheng, H.[Hong], Yuan, Z.J.[Ze-Jian], Chen, B.D.[Ba-Dong],
Maximum Correntropy Criterion-Based Sparse Subspace Learning for Unsupervised Feature Selection,
CirSysVideo(29), No. 2, February 2019, pp. 404-417.
IEEE DOI 1902
Feature extraction, Robustness, Kernel, Convergence, Computational modeling, Measurement, Machine learning, sparse subspace learning BibRef

Yang, B.[Ben], Wu, J.H.[Jing-Han], Zhou, Y.[Yu], Zhang, X.T.[Xue-Tao], Lin, Z.P.[Zhi-Ping], Nie, F.P.[Fei-Ping], Chen, B.D.[Ba-Dong],
Robust spectral embedded bilateral orthogonal concept factorization for clustering,
PR(150), 2024, pp. 110308.
Elsevier DOI 2403
Concept factorization, Spectral embedding, Correntropy, Clustering BibRef

Liao, W., Pizurica, A., Scheunders, P., Philips, W., Pi, Y.,
Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images,
GeoRS(51), No. 1, January 2013, pp. 184-198.
IEEE DOI 1301
BibRef

Schiezaro, M.[Mauricio], Pedrini, H.[Helio],
Data feature selection based on Artificial Bee Colony algorithm,
JIVP(2013), No. 1, 2013, pp. 47.
DOI Link 1309
BibRef

Wang, L.[Ling], Cheng, H.[Hong], Liu, Z.C.[Zi-Cheng], Zhu, C.[Ce],
A robust elastic net approach for feature learning,
JVCIR(25), No. 2, 2014, pp. 313-321.
Elsevier DOI 1402
Feature learning BibRef

Bandyopadhyay, S.[Sanghamitra], Bhadra, T.[Tapas], Mitra, P.[Pabitra], Maulik, U.[Ujjwal],
Integration of dense subgraph finding with feature clustering for unsupervised feature selection,
PRL(40), No. 1, 2014, pp. 104-112.
Elsevier DOI 1403
Pattern recognition BibRef

Zhu, P.F.[Peng-Fei], Zuo, W.M.[Wang-Meng], Zhang, L.[Lei], Hu, Q.H.[Qing-Hua], Shiu, S.C.K.[Simon C.K.],
Unsupervised feature selection by regularized self-representation,
PR(48), No. 2, 2015, pp. 438-446.
Elsevier DOI 1411
Self-representation BibRef

Zhu, P.F.[Peng-Fei], Zhu, W.C.[Wen-Cheng], Wang, W.Z.[Wei-Zhi], Zuo, W.M.[Wang-Meng], Hu, Q.H.[Qing-Hua],
Non-convex regularized self-representation for unsupervised feature selection,
IVC(60), No. 1, 2017, pp. 22-29.
Elsevier DOI 1704
Self-representation BibRef

Zhu, P.F.[Peng-Fei], Xu, Q.[Qian], Hu, Q.H.[Qing-Hua], Zhang, C.Q.[Chang-Qing], Zhao, H.[Hong],
Multi-label feature selection with missing labels,
PR(74), No. 1, 2018, pp. 488-502.
Elsevier DOI 1711
Feature selection BibRef

Liang, S., Xu, Q.[Qian], Zhu, P.F.[Peng-Fei], Hu, Q.H.[Qing-Hua], Zhang, C.Q.[Chang-Qing],
Unsupervised feature selection by manifold regularized self-representation,
ICIP17(2398-2402)
IEEE DOI 1803
Clustering algorithms, Face, Feature extraction, Laplace equations, Manifolds, Optimization, Signal processing algorithms, Unsupervised feature selection BibRef

Zhu, P.F.[Peng-Fei], Zhu, W.C.[Wen-Cheng], Hu, Q.H.[Qing-Hua], Zhang, C.Q.[Chang-Qing], Zuo, W.M.[Wang-Meng],
Subspace clustering guided unsupervised feature selection,
PR(66), No. 1, 2017, pp. 364-374.
Elsevier DOI 1704
Subspace clustering BibRef

Zhang, F.[Fan], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Saliency-Guided Unsupervised Feature Learning for Scene Classification,
GeoRS(53), No. 4, April 2015, pp. 2175-2184.
IEEE DOI 1502
feature extraction BibRef

Yao, J.[Jin], Mao, Q.[Qi], Goodison, S.[Steve], Mai, V.[Volker], Sun, Y.J.[Yi-Jun],
Feature selection for unsupervised learning through local learning,
PRL(53), No. 1, 2015, pp. 100-107.
Elsevier DOI 1502
Feature selection BibRef

Li, Z.C.[Ze-Chao], Tang, J.H.[Jin-Hui],
Unsupervised Feature Selection via Nonnegative Spectral Analysis and Redundancy Control,
IP(24), No. 12, December 2015, pp. 5343-5355.
IEEE DOI 1512
feature selection BibRef

Akay, B.[Bahriye], Karaboga, D.[Dervis],
A survey on the applications of artificial bee colony in signal, image, and video processing,
SIViP(9), No. 4, May 2015, pp. 967-990.
WWW Link. 1504
Survey, Bee Colony. BibRef

Xu, Y., Qiu, P., Roysam, B.,
Unsupervised Discovery of Subspace Trends,
PAMI(37), No. 10, October 2015, pp. 2131-2145.
IEEE DOI 1509
Algorithm design and analysis BibRef

Han, J.Q.[Jiu-Qi], Sun, Z.Y.[Zheng-Ya], Hao, H.W.[Hong-Wei],
L0-norm based structural sparse least square regression for feature selection,
PR(48), No. 12, 2015, pp. 3927-3940.
Elsevier DOI 1509
Structural sparse learning BibRef

Feng, J.[Jie], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Sun, T.[Tao], Zhang, X.R.[Xiang-Rong],
Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images,
PR(51), No. 1, 2016, pp. 295-309.
Elsevier DOI 1601
Unsupervised feature selection BibRef

Xu, L., Wong, A., Li, F., Clausi, D.A.,
Intrinsic Representation of Hyperspectral Imagery for Unsupervised Feature Extraction,
GeoRS(54), No. 2, February 2016, pp. 1118-1130.
IEEE DOI 1601
Correlation BibRef

Alba-Cabrera, E.[Eduardo], Godoy-Calderon, S.[Salvador], Ibarra-Fiallo, J.[Julio],
Generating synthetic test matrices as a benchmark for the computational behavior of typical testor-finding algorithms,
PRL(80), No. 1, 2016, pp. 46-51.
Elsevier DOI 1609
Feature selection BibRef

Wang, D.[Dong], Tan, X.Y.[Xiao-Yang],
Unsupervised feature learning with C-SVDDNet,
PR(60), No. 1, 2016, pp. 473-485.
Elsevier DOI 1609
Unsupervised feature learning BibRef

Wen, J.J.[Jia-Jun], Lai, Z.H.[Zhi-Hui], Zhan, Y.W.[Yin-Wei], Cui, J.R.[Jin-Rong],
The L2,1-norm-based unsupervised optimal feature selection with applications to action recognition,
PR(60), No. 1, 2016, pp. 515-530.
Elsevier DOI 1609
Feature selection BibRef

Wen, J.J.[Jia-Jun], Lai, Z.H.[Zhi-Hui], Wong, W.K.[Wai Keung], Cui, J.R.[Jin-Rong], Wan, M.H.[Ming-Hua],
Optimal Feature Selection for Robust Classification via L2,1-Norms Regularization,
ICPR14(517-521)
IEEE DOI 1412
Accuracy, Convergence, Face, Face recognition, Robustness, Training, Vectors BibRef

Mo, D.M.[Dong-Mei], Lai, Z.H.[Zhi-Hui],
Robust Jointly Sparse Regression with Generalized Orthogonal Learning for Image Feature Selection,
PR(93), 2019, pp. 164-178.
Elsevier DOI 1906
Code, Matlab.
WWW Link. Dimensionality reduction, Local structure, Joint sparsity, Orthogonality, Orthogonal matching pursuit BibRef

Xiong, W.[Wei], Zhang, L.[Lefei], Du, B.[Bo], Tao, D.C.[Da-Cheng],
Combining local and global: Rich and robust feature pooling for visual recognition,
PR(62), No. 1, 2017, pp. 225-235.
Elsevier DOI 1705
Unsupervised learning BibRef

Zhang, Z.H.[Zhi-Hong], Bai, L.[Lu], Liang, Y.H.[Yuan-Heng], Hancock, E.R.[Edwin R.],
Joint hypergraph learning and sparse regression for feature selection,
PR(63), No. 1, 2017, pp. 291-309.
Elsevier DOI 1612
BibRef
Earlier:
Adaptive Graph Learning for Unsupervised Feature Selection,
CAIP15(I:790-800).
Springer DOI 1511
BibRef
And:
Unsupervised Feature Selection by Graph Optimization,
CIAP15(I:130-140).
Springer DOI 1511
Feature selection BibRef

Zhang, Z.H.[Zhi-Hong], Xiahou, J.B.[Jian-Bing], Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Coupled-Feature Hypergraph Representation for Feature Selection,
GbRPR15(44-53).
Springer DOI 1511
BibRef

Zaharieva, M.[Maia], Breiteneder, C.[Christian], Hudec, M.[Marcus],
Unsupervised group feature selection for media classification,
MultInfoRetr(6), No. 3, September 2017, pp. 233-249.
Springer DOI 1708
BibRef

Solorio-Fernández, S.[Saúl], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[J. Ariel],
A new Unsupervised Spectral Feature Selection Method for mixed data: A filter approach,
PR(72), No. 1, 2017, pp. 314-326.
Elsevier DOI 1708
Unsupervised, feature, selection BibRef

Zhao, L., Chen, Z., Wang, Z.J.,
Unsupervised Multiview Nonnegative Correlated Feature Learning for Data Clustering,
SPLetters(25), No. 1, January 2018, pp. 60-64.
IEEE DOI 1801
correlation methods, image representation, optimisation, pattern clustering, unsupervised learning, UMCFL method, unsupervised learning BibRef

Li, Y.D.[Yang-Ding], Lei, C.[Cong], Fang, Y.[Yue], Hu, R.Y.[Rong-Yao], Li, Y.G.[Yong-Gang], Zhang, S.C.[Shi-Chao],
Unsupervised feature selection by combining subspace learning with feature self-representation,
PRL(109), 2018, pp. 35-43.
Elsevier DOI 1806
Subspace learning, Feature selection, Self-representation BibRef

Zhu, Q.H.[Qi-Hai], Yang, Y.B.[Yu-Bin],
Discriminative embedded unsupervised feature selection,
PRL(112), 2018, pp. 219-225.
Elsevier DOI 1809
Unsupervised learning, Feature selection, Laplacian regularization, Discriminative clustering, Simplex learning BibRef

Zhou, P.[Peng], Hu, X.G.[Xue-Gang], Li, P.P.[Pei-Pei], Wu, X.D.[Xin-Dong],
OFS-Density: A novel online streaming feature selection method,
PR(86), 2019, pp. 48-61.
Elsevier DOI 1811
Feature selection, Online feature selection, Streaming features, Neighborhood rough set BibRef

Teisseyre, P.[Pawel], Zufferey, D.[Damien], Slomka, M.[Marta],
Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification,
PR(86), 2019, pp. 290-319.
Elsevier DOI 1811
Multi-label classification, Cost-sensitive feature selection, Classifier chains, Logistic regression, Stability, Generalization error bounds BibRef

Su, Y.T.[Yu-Ting], Bai, X.[Xu], Li, W.[Wu], Jing, P.G.[Pei-Guang], Zhang, J.[Jing], Liu, J.[Jing],
Graph regularized low-rank tensor representation for feature selection,
JVCIR(56), 2018, pp. 234-244.
Elsevier DOI 1811
Unsupervised feature selection, Low-rank tensor representation, Graph embedding, Subspace clustering BibRef

Bradley, P.E.[Patrick Erik], Keller, S.[Sina], Weinmann, M.[Martin],
Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Kashef, S.[Shima], Nezamabadi-pour, H.[Hossein],
A label-specific multi-label feature selection algorithm based on the Pareto dominance concept,
PR(88), 2019, pp. 654-667.
Elsevier DOI 1901
Multi-label dataset, Feature selection, Label-specific features, Pareto dominance, Online feature selection BibRef

Yuan, H.L.[Hao-Liang], Li, J.Y.[Jun-Yu], Lai, L.L.[Loi Lei], Tang, Y.Y.[Yuan Yan],
Joint sparse matrix regression and nonnegative spectral analysis for two-dimensional unsupervised feature selection,
PR(89), 2019, pp. 119-133.
Elsevier DOI 1902
Unsupervised learning, Two-dimensional feature selection, Sparse matrix regression, Nonnegative spectral analysis BibRef

Wang, H.[Haishuai], Zhang, Q.[Qin], Wu, J.[Jia], Pan, S.R.[Shi-Rui], Chen, Y.X.[Yi-Xin],
Time series feature learning with labeled and unlabeled data,
PR(89), 2019, pp. 55-66.
Elsevier DOI 1902
Time series, Feature selection, Semi-supervised learning, Classification BibRef

Nie, F., Yang, S., Zhang, R., Li, X.,
A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization,
IP(28), No. 5, May 2019, pp. 2428-2438.
IEEE DOI 1903
data mining, feature extraction, feature selection, graph theory, learning (artificial intelligence), minimisation, redundant features BibRef

de Amorim, R.C.[Renato Cordeiro],
Unsupervised feature selection for large data sets,
PRL(128), 2019, pp. 183-189.
Elsevier DOI 1912
Unsupervised feature selection, Clustering, Big data BibRef

Li, X., Zhang, H., Zhang, R., Nie, F.,
Discriminative and Uncorrelated Feature Selection With Constrained Spectral Analysis in Unsupervised Learning,
IP(29), 2020, pp. 2139-2149.
IEEE DOI 2001
Feature extraction, Spectral analysis, Unsupervised learning, Optimization, Manifolds, Linear programming, Task analysis, unsupervised learning BibRef

Guo, M., Yang, S., Nie, F., Li, X.,
Locality-Based Discriminant Feature Selection with Trace Ratio,
ICIP18(3373-3377)
IEEE DOI 1809
Feature extraction, Robustness, Linear programming, Data structures, Power capacitors, Optical filters, Optimization, local data structure BibRef

Yang, S., Nie, F., Li, X.,
Unsupervised Feature Selection with Local Structure Learning,
ICIP18(3398-3402)
IEEE DOI 1809
Feature extraction, Eigenvalues and eigenfunctions, Laplace equations, Sparse matrices, Face, Optical imaging, feature selection BibRef

Yan, X.Y.[Xu-Yang], Nazmi, S.[Shabnam], Erol, B.A.[Berat A.], Homaifar, A.[Abdollah], Gebru, B.[Biniam], Tunstel, E.[Edward],
An efficient unsupervised feature selection procedure through feature clustering,
PRL(131), 2020, pp. 277-284.
Elsevier DOI 2004
Unsupervised feature selection, Feature clustering, Feature redundancy BibRef

Gan, J.Z.[Jiang-Zhang], Wen, G.Q.[Guo-Qiu], Yu, H.[Hao], Zheng, W.[Wei], Lei, C.[Cong],
Supervised feature selection by self-paced learning regression,
PRL(132), 2020, pp. 30-37.
Elsevier DOI 2005
Feature selection, Self-paced learning, Regression analysis, Supervised learning, Sparse learning BibRef

Zheng, W.[Wei], Zhu, X.F.[Xiao-Feng], Wen, G.Q.[Guo-Qiu], Zhu, Y.H.[Yong-Hua], Yu, H.[Hao], Gan, J.Z.[Jiang-Zhang],
Unsupervised feature selection by self-paced learning regularization,
PRL(132), 2020, pp. 4-11.
Elsevier DOI 2005
Feature selection, Self-paced learning, Robust statistic BibRef

Zhang, R., Li, X.,
Unsupervised Feature Selection Via Data Reconstruction and Side Information,
IP(29), 2020, pp. 8097-8106.
IEEE DOI 2008
Feature extraction, Robustness, Image reconstruction, Manifolds, Laplace equations, Minimization, Data models, Feature selection, the~graph embedding BibRef

Lim, H.K.[Hyun-Ki], Kim, D.W.[Dae-Won],
Pairwise dependence-based unsupervised feature selection,
PR(111), 2021, pp. 107663.
Elsevier DOI 2012
Unsupervised feature selection, Feature dependency, Feature redundancy, Joint entropy, regularization BibRef

Wu, J.S.[Jian-Sheng], Song, M.X.[Meng-Xiao], Min, W.D.[Wei-Dong], Lai, J.H.[Jian-Huang], Zheng, W.S.[Wei-Shi],
Joint adaptive manifold and embedding learning for unsupervised feature selection,
PR(112), 2021, pp. 107742.
Elsevier DOI 2102
Unsupervised feature selection, Manifold learning, Embedding learning, Sparse learning BibRef

Shang, R.H.[Rong-Hua], Wang, L.J.[Lu-Juan], Shang, F.H.[Fan-Hua], Jiao, L.C.[Li-Cheng], Li, Y.Y.[Yang-Yang],
Dual space latent representation learning for unsupervised feature selection,
PR(114), 2021, pp. 107873.
Elsevier DOI 2103
Latent representation learning, Unsupervised feature selection, Dual space, Sparse regression BibRef

Song, Z.H.[Zi-Hao], Song, P.[Peng], Sheng, C.[Chao], Zheng, W.M.[Wen-Ming], Zhang, W.J.[Wen-Jing], Li, S.[Shaokai],
A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection,
IEICE(E105-D), No. 1, January 2022, pp. 175-179.
WWW Link. 2201
BibRef

Huang, P.[Pei], Yang, X.W.[Xiao-Wei],
Unsupervised feature selection via adaptive graph and dependency score,
PR(127), 2022, pp. 108622.
Elsevier DOI 2205
Unsupervised feature selection, Adaptive graph, Mutual information, Entropy BibRef

Liu, K.H.[Keng-Hao], Chen, Y.K.[Yu-Kai], Chen, T.Y.[Tsun-Yang],
A Band Subset Selection Approach Based on Sparse Self-Representation and Band Grouping for Hyperspectral Image Classification,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Chien, H.C.[Hung-Chang], Lai, C.H.[Chih-Hung], Liu, K.H.[Keng-Hao],
Unsupervised Band Selection Based on Group-Based Sparse Representation,
HISP16(I: 389-401).
Springer DOI 1704
BibRef

Chen, B.[Bilian], Guan, J.[Jiewen], Li, Z.N.[Zhe-Ning],
Unsupervised Feature Selection via Graph Regularized Nonnegative CP Decomposition,
PAMI(45), No. 2, February 2023, pp. 2582-2594.
IEEE DOI 2301
Feature extraction, Tensors, Sparse matrices, Data models, Principal component analysis, Optimization, Matrix decomposition, classification BibRef

Shi, D.[Dan], Zhu, L.[Lei], Li, J.J.[Jing-Jing], Zhang, Z.[Zheng], Chang, X.J.[Xiao-Jun],
Unsupervised Adaptive Feature Selection With Binary Hashing,
IP(32), 2023, pp. 838-853.
IEEE DOI 2301
Feature extraction, Codes, Spectral analysis, Semantics, Task analysis, Adaptation models, Correlation, Adaptive, unsupervised feature selection BibRef

Li, Z.X.[Zheng-Xin], Nie, F.P.[Fei-Ping], Bian, J.[Jintang], Wu, D.Y.[Dan-Yang], Li, X.L.[Xue-Long],
Sparse PCA via L_2,p-Norm Regularization for Unsupervised Feature Selection,
PAMI(45), No. 4, April 2023, pp. 5322-5328.
IEEE DOI 2303
Feature extraction, Principal component analysis, Sparse matrices, Optimization, Task analysis, Minimization, sparse learning BibRef

Wang, Z.[Zheng], Li, Q.[Qi], Zhao, H.F.[Hai-Feng], Nie, F.P.[Fei-Ping],
Simultaneous local clustering and unsupervised feature selection via strong space constraint,
PR(142), 2023, pp. 109718.
Elsevier DOI 2307
Unsupervised feature selection, -Norm constraint optimization, Local structure learning BibRef

Huang, P.[Pei], Kong, Z.M.[Zhao-Ming], Xie, M.[Mengying], Yang, X.W.[Xiao-Wei],
Robust unsupervised feature selection via data relationship learning,
PR(142), 2023, pp. 109676.
Elsevier DOI 2307
Unsupervised feature selection, Outlier, Robustness BibRef

Guo, L.L.[Ling-Li], Chen, X.H.[Xiu-Hong],
Latent low-rank representation sparse regression model with symmetric constraint for unsupervised feature selection,
IET-IPR(17), No. 9, 2023, pp. 2791-2805.
DOI Link 2307
face recognition, feature selection, image representation, pattern clustering, regression analysis BibRef

Wang, C.C.[Chen-Chen], Wang, J.[Jun], Gu, Z.[Zhichen], Wei, J.M.[Jin-Mao], Liu, J.[Jian],
Unsupervised feature selection by learning exponential weights,
PR(148), 2024, pp. 110183.
Elsevier DOI 2402
Unsupervised feature selection, Sparse regression, Local structure learning, Global information preservation BibRef

Li, D.Z.[Duan-Zhang], Chen, H.M.[Hong-Mei], Mi, Y.[Yong], Luo, C.[Chuan], Horng, S.J.[Shi-Jinn], Li, T.R.[Tian-Rui],
Dual space-based fuzzy graphs and orthogonal basis clustering for unsupervised feature selection,
PR(155), 2024, pp. 110683.
Elsevier DOI 2408
Unsupervised feature selection, Dual-graph, Orthogonal basis clustering, Sparse learning BibRef

Wang, M., Yue, X., Gao, C., Chen, Y.,
Feature Selection Ensemble for Symbolic Data Classification with AHP,
ICPR18(868-873)
IEEE DOI 1812
Feature extraction, Analytic hierarchy process, Distributed databases, Linear matrix inequalities, Task analysis, analytic hierarchy process BibRef

Zheng, J., Lee, T., Feng, C., Lit, X., Zhang, Z.,
Robust Attentional Pooling via Feature Selection,
ICPR18(2038-2043)
IEEE DOI 1812
Feature extraction, Visualization, Image coding, Solid modeling BibRef

Wei, R.[Ran], Robles-Kelly, A.[Antonio], Álvarez, J.[José],
Context Free Band Reduction Using a Convolutional Neural Network,
SSSPR18(86-96).
Springer DOI 1810
BibRef

Wangila, K.W., Gao, K., Zhu, P., Hu, Q., Zhang, C.,
Mixed sparsity regularized multi-view unsupervised feature selection,
ICIP17(1930-1934)
IEEE DOI 1803
Convergence, Data models, Feature extraction, Laplace equations, Noise measurement, Optimization, Social network services, unsupervised feature selection BibRef

Zhuge, W.Z.[Wen-Zhang], Hou, C., Nie, F., Yi, D.,
Unsupervised feature extraction using a learned graph with clustering structure,
ICPR16(3597-3602)
IEEE DOI 1705
Algorithm design and analysis, Clustering algorithms, Concrete, Eigenvalues and eigenfunctions, Feature extraction, Laplace equations, Learning systems, clustering information, feature extraction, learned, graph BibRef

Nie, S.Q.[Si-Qi], Gao, T.[Tian], Ji, Q.A.[Qi-Ang],
An information theoretic feature selection framework based on integer programming,
ICPR16(3584-3589)
IEEE DOI 1705
Computers, Entropy, Feature extraction, Linear programming, Mutual information, Systems, engineering, and, theory BibRef

Rani, D.S., Rani, T.S., Bhavani, S.D.,
Feature subset selection using consensus clustering,
ICAPR15(1-6)
IEEE DOI 1511
feature selection BibRef

Majumder, A., Hasanuzzaman, M., Ekbal, A.,
Feature selection for event extraction in biomedical text,
ICAPR15(1-6)
IEEE DOI 1511
data mining BibRef

Han, D.Y.[Dong-Yoon], Kim, J.[Junmo],
Unsupervised Simultaneous Orthogonal basis Clustering Feature Selection,
CVPR15(5016-5023)
IEEE DOI 1510
BibRef

Sui, C.H.[Chen-Hong], Tian, Y.[Yan], Xu, Y.P.[Yi-Ping],
An Unsupervised Band Selection Method Based on Overall Accuracy Prediction,
ICPR14(3756-3761)
IEEE DOI 1412
Accuracy BibRef

Lan, T.[Tian], Raptis, M.[Michalis], Sigal, L.[Leonid], Mori, G.[Greg],
From Subcategories to Visual Composites: A Multi-level Framework for Object Detection,
ICCV13(369-376)
IEEE DOI 1403
Appearence changes with pose. Subcategories automatically, object class (car) given. BibRef

Liu, Y.[Yang], Wang, Y.Z.[Yi-Zhou],
Unsupervised discriminative feature selection in a kernel space via L2,1-norm minimization,
ICPR12(1205-1208).
WWW Link. 1302
BibRef

Coelho, F.[Frederico], Braga, A.P.[Antonio Padua], Verleysen, M.[Michel],
Multi-Objective Semi-Supervised Feature Selection and Model Selection Based on Pearson's Correlation Coefficient,
CIARP10(509-516).
Springer DOI 1011
BibRef

Wang, S.Y.[Sui-Yu], Baird, H.S.[Henry S.],
Performance Evaluation of Automatic Feature Discovery Focused within Error Clusters,
ICPR10(718-721).
IEEE DOI 1008
BibRef
Earlier:
Feature selection focused within error clusters,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhao, B.[Bin], Kwok, J.T.[James T.], Wang, F.[Fei], Zhang, C.S.[Chang-Shui],
Unsupervised Maximum Margin Feature Selection with manifold regularization,
CVPR09(888-895).
IEEE DOI 0906
BibRef

Xie, L.X.[Le-Xing], Chang, S.F.[Shih-Fu], Divakaran, A., Sun, H.F.[Hui-Fang],
Feature selection for unsupervised discovery of statistical temporal structures in video,
ICIP03(I: 29-32).
IEEE DOI 0312
BibRef

Murphey, Y.L., Guo, H.,
Automatic Feature Selection: A Hybrid Statistical Approach,
ICPR00(Vol II: 382-385).
IEEE DOI 0009
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Feature Selection using Search and Learning .