14.1.3.1 Unsupervised Feature Selection

Chapter Contents (Back)
Feature Selection. Dimensionality. Wide variation of topics -- consider dividing it.

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

Hong, Y.[Yi], Kwong, S.[Sam], Chang, Y.C.[Yu-Chou], Ren, Q.S.[Qing-Sheng],
Consensus unsupervised feature ranking from multiple views,
PRL(29), No. 5, 1 April 2008, pp. 595-602.
Elsevier DOI 0802
Clustering; Feature ranking ensembles; Unsupervised feature selection 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

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], 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.[Zechao], 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

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 Fco.], 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


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

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.[Siqi], 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.[Chenhong], Tian, Y.[Yan], Xu, Y.[Yiping],
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

Shankar, S.[Sukrit], Lasenby, J.[Joan], Cipolla, R.[Roberto],
Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes,
ICCV13(361-368)
IEEE DOI 1403
Ranking attributes for classification. Optimization; Ranking; Semantic Descriptions 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

Li, Y.[Yun], Lu, B.L.[Bao-Liang], Wu, Z.F.[Zhong-Fu],
A Hybrid Method of Unsupervised Feature Selection Based on Ranking,
ICPR06(II: 687-690).
IEEE DOI 0609
BibRef

Chang, S.R.[Shao-Rong], Dasgupta, N.[Nilanjan], Carin, L.[Lawrence],
A Bayesian Approach to Unsupervised Feature Selection and Density Estimation Using Expectation Propagation,
CVPR05(II: 1043-1050).
IEEE DOI 0507
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 .


Last update:Sep 22, 2017 at 21:00:01