14.1.3 Feature Selection in Pattern Recognition or Clustering

Chapter Contents (Back)
Feature Selection. Classification. Pattern Recognition. Deciding which features are relevant for a classification task is feature selection. This includes a lot of very similar ways. See also Unsupervised Feature Selection. See also Feature Selection using Search and Learning.

Andrews, H.C.,
Multidimensional Rotations in Feature Selection,
TC(20), No. 9, September 1971, pp. 1045. BibRef 7109

Fukunaga, K., Koontz, W.L.G.,
Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering,
TC(19), No. 4, April 1970, pp. 311. See also Algorithm for Finding Intrinsic Dimensionality of Data, An. BibRef 7004

Rasek, E.,
A contribution to the problem of feature selection with similarity functionals in pattern recognition,
PR(3), No. 1, April 1971, pp. 31-36.
Elsevier DOI 0309
BibRef

Kittler, J.V.[Josef V.], Young, P.C.[Peter C.],
A new approach to feature selection based on the Karhunen-Loeve expansion,
PR(5), No. 4, December 1973, pp. 335-352.
Elsevier DOI 0309
BibRef

Chen, C.H.,
On a class of computationally efficient feature selection criteria,
PR(7), No. 1-2, June 1975, pp. 87-94.
Elsevier DOI 0309
BibRef

Kowalski, B.R., Bender, C.F.,
An orthogonal feature selection method,
PR(8), No. 1, January 1976, pp. 1-4.
Elsevier DOI 0309
BibRef

Christensen, R., Reichert, T.,
Unit measure violations in pattern recognition: Ambiguity and irrelevancy,
PR(8), No. 4, October 1976, pp. 239-245.
Elsevier DOI 0309
BibRef

Decell, Jr., H.P., Guseman, Jr., L.F.,
Linear feature selection with applications,
PR(11), No. 1, 1979, pp. 55-63.
Elsevier DOI 0309
BibRef

Bryant, J.[Jack], Guseman, Jr., L.F.,
Distance preserving linear feature selection,
PR(11), No. 5-6, 1979, pp. 347-352.
Elsevier DOI 0309
BibRef

Boekee, D.E., van der Lubbe, J.C.A.,
Some aspects of error bounds in feature selection,
PR(11), No. 5-6, 1979, pp. 353-360.
Elsevier DOI 0309
BibRef

Peters, C.[Charles],
Feature selection for best mean square approximation of class densities,
PR(11), No. 5-6, 1979, pp. 361-364.
Elsevier DOI 0309
BibRef

Young, D.M.[Dean M.], Odell, P.L.[Patrick L.],
A formulation and comparison of two linear feature selection techniques applicable to statistical classification,
PR(17), No. 3, 1984, pp. 331-337.
Elsevier DOI 0309
BibRef

Hester, C.F., Casasent, D.,
Multivariant Techniques for Multiclass Pattern Recognition,
AppOpt(19), 1980, pp, 1758-1761. BibRef 8000

Wismath, S.K., Soong, H.P., Akl, S.G.,
Feature selection by interactive clustering,
PR(14), No. 1-6, 1981, pp. 75-80.
Elsevier DOI 0309
BibRef

di Gesù, V., Maccarone, M.C.,
Features selection and 'possibility theory',
PR(19), No. 1, 1986, pp. 63-72.
Elsevier DOI 0309
BibRef

Bidasaria, H.B.,
Least desirable feature elimination in a general pattern recognition problem,
PR(20), No. 3, 1987, pp. 365-370.
Elsevier DOI 0309
BibRef

Morgera, S.D., and Datta, L.,
Toward a Fundamental Theory of Optimal Feature Selection: Part I,
PAMI(6), No. 5, September 1984, pp. 601-616. BibRef 8409
Earlier:
Optimal Feature Selection: Part I Theory,
ICPR84(134-137). BibRef

Morgera, S.D.,
Toward a Fundamental Theory of Optimal Feature Selection: Part II -- Implementation and Computational Complexity,
PAMI(9), No. 1, January 1987, pp. 29-38. BibRef 8701

Datta, L., and Morgera, S.D.,
Optimal Feature Selection: Part II -- Implementation,
ICPR84(138-141). BibRef 8400

Malina, W.,
On an Extended Fischer Criterion for Feature Selection,
PAMI(3), 1981, pp. 611-614. BibRef 8100

Kudo, M.[Mineichi], Shimbo, M.[Masaru],
Feature selection based on the structural indices of categories,
PR(26), No. 6, June 1993, pp. 891-901.
Elsevier DOI 0401
BibRef

Siddiqui, K.J., Liu, Y.H., Hay, D.R., Suen, C.Y.,
Feature-Selection Using A Proximity-Index Optimization Model,
PRL(15), No. 11, November 1994, pp. 1137-1141. BibRef 9411

Krishnan, S., Samudravijaya, K., Rao, P.V.S.,
Feature-Selection for Pattern-Classification with Gaussian Mixture-Models: A New Objective Criterion,
PRL(17), No. 8, July 1 1996, pp. 803-809. 9608
BibRef

Thawonmas, R., Abe, S.,
A Novel-Approach to Feature-Selection Based on Analysis of Class Regions,
SMC-B(27), No. 2, April 1997, pp. 196-207.
IEEE Top Reference. 9704
BibRef

Jelonek, J., Stefanowski, J.,
Feature Subset-Selection for Classification of Histological Images,
AIMed(9), No. 3, March 1997, pp. 227-239. 9704
BibRef

Hardie, R.C., Vaidyanathan, M., McManamon, F.,
Spectral Band Selection and Classifier Design for a Multispectral Imaging Laser-Radar,
OptEng(37), No. 3, March 1998, pp. 752-762. 9804
BibRef

Holz, H.J., Loew, M.H.,
Multiclass Classifier Independent Feature Analysis,
PRL(18), No. 11-13, November 1997, pp. 1219-1224. 9806
BibRef

Cozzi, A., Worgotter, F.,
Reclustering Techniques Improve Early Vision Feature Maps,
PAA(1), No. 1, 1998, pp. 42-51. BibRef 9800

Klimesova, D., Saic, S.,
Feature Selection Algorithm and Cobweb Correlation,
PRL(19), No. 8, June 1998, pp. 681-685. 9808
BibRef

Meyer-Baese, A.[Anke], Watzel, R.[Rolf],
Transformation radial basis neural network for relevant feature selection,
PRL(19), No. 14, December 1998, pp. 1301-1306. BibRef 9812

Shapira, Y.[Yair], Gath, I.[Isak],
Feature selection for multiple binary classification problems,
PRL(20), No. 8, August 1999, pp. 823-832. BibRef 9908

Brunzell, H., Eriksson, J.,
Feature reduction for classification of multidimensional data,
PR(33), No. 10, October 2000, pp. 1741-1748.
Elsevier DOI 0006
BibRef

Kudo, M.[Mineichi], Sklansky, J.[Jack],
Comparison of algorithms that select features for pattern classifiers,
PR(33), No. 1, January 2000, pp. 25-41.
Elsevier DOI 0005
BibRef

Vishwanathan, S.V.N., Murty, M.N.[M. Narasimha],
Kohonen's SOM with cache,
PR(33), No. 11, November 2000, pp. 1927-1929.
Elsevier DOI 0011
BibRef

Last, M.[Mark], Kandel, A.[Abraham], Maimon, O.[Oded],
Information-theoretic algorithm for feature selection,
PRL(22), No. 6-7, May 2001, pp. 799-811.
Elsevier DOI 0105
BibRef

Yang, J.[Jian], Yang, J.Y.[Jing-Yu],
Generalized K-L transform based combined feature extraction,
PR(35), No. 1, January 2002, pp. 295-297.
Elsevier DOI 0111
BibRef

Sebban, M.[Marc], Nock, R.[Richard],
A hybrid filter/wrapper approach of feature selection using information theory,
PR(35), No. 4, April 2002, pp. 835-846.
Elsevier DOI 0201
BibRef

Devi, V.S.[V. Susheela], Murty, M.N.[M. Narasimha],
An incremental prototype set building technique,
PR(35), No. 2, February 2002, pp. 505-513.
Elsevier DOI 0201
BibRef

Swiniarski, R.W.[Roman W.], Skowron, A.[Andrzej],
Rough set methods in feature selection and recognition,
PRL(24), No. 6, March 2003, pp. 833-849.
Elsevier DOI 0301
BibRef

Skowron, A.[Andrzej], Bazan, J.[Jan], Wojnarski, M.[Marcin],
Interactive Rough-Granular Computing in Pattern Recognition,
PReMI09(92-97).
Springer DOI 0912
BibRef

Skowron, A.[Andrzej],
Discovery of Process Models from Data and Domain Knowledge: A Rough-Granular Approach,
PReMI07(192-197).
Springer DOI 0712
BibRef

Choi, E.[Euisun], Lee, C.H.[Chul-Hee],
Feature extraction based on the Bhattacharyya distance,
PR(36), No. 8, August 2003, pp. 1703-1709.
Elsevier DOI 0304
BibRef
Earlier: A2, A1:
Optimizing Feature Extraction for Multiclass Problems,
ICPR00(Vol II: 402-405).
IEEE DOI 0009
BibRef

Dong, M.[Ming], Kothari, R.[Ravi],
Feature subset selection using a new definition of classifiability,
PRL(24), No. 9-10, June 2003, pp. 1215-1225.
Elsevier DOI 0304
BibRef

Bressan, M.[Marco], Vitria, J.[Jordi],
On the selection and classification of independent features,
PAMI(25), No. 10, October 2003, pp. 1312-1317.
IEEE Abstract. 0310
Feature selection when classes are modeled by statistically independent features. BibRef

Li, J.[Jie], Wang, J.X.[Jia-Xin], Zhao, Y.[Yannan], Yang, Z.[Zehong],
Self-adaptive design of hidden Markov models,
PRL(25), No. 2, January 2004, pp. 197-210.
Elsevier DOI 0401
BibRef

McCane, B.[Brendan], Caelli, T.M.[Terry M.],
Diagnostic tools for evaluating and updating hidden Markov models,
PR(37), No. 7, July 2004, pp. 1325-1337.
Elsevier DOI 0405
BibRef
Earlier: A2, A1:
Components analysis of hidden Markov models in computer vision,
CIAP03(510-515).
IEEE DOI 0310
How parameter and topology estimation contribute. BibRef

Shen, Q.A.[Qi-Ang], Jensen, R.[Richard],
Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring,
PR(37), No. 7, July 2004, pp. 1351-1363.
Elsevier DOI 0405
BibRef

Valev, V.[Ventzeslav], Sankur, B.[Bulent],
Generalized Non-reducible Descriptors,
PR(37), No. 9, September 2004, pp. 1809-1815.
Elsevier DOI 0407
BibRef
Earlier: Add A2: Radeva, P.I., ICPR00(Vol II: 394-397).
IEEE DOI 0009
BibRef

Asaithambi, A.[Asai], Valev, V.[Ventzeslav],
Construction of all non-reducible descriptors,
PR(37), No. 9, September 2004, pp. 1817-1823.
Elsevier DOI 0407
BibRef

Ding, X., He, L., Carin, L.[Lawrence],
Bayesian Robust Principal Component Analysis,
IP(20), No. 12, December 2011, pp. 3419-3430.
IEEE DOI 1112
BibRef

Ahmad, A.[Amir], Dey, L.[Lipika],
A feature selection technique for classificatory analysis,
PRL(26), No. 1, 1 January 2005, pp. 43-56.
Elsevier DOI 0501
BibRef

Shih, F.Y.[Frank Y.], Cheng, S.X.[Shou-Xian],
Improved feature reduction in input and feature spaces,
PR(38), No. 5, May 2005, pp. 651-659.
Elsevier DOI 0501
See also improved incremental training algorithm for support vector machines using active query, An. BibRef

Hsing, T.[Tailen], Liu, L.Y.[Li-Yu], Brun, M.[Marcel], Dougherty, E.R.[Edward R.],
The coefficient of intrinsic dependence (feature selection using el CID),
PR(38), No. 5, May 2005, pp. 623-636.
Elsevier DOI 0501
BibRef

Silva, P.J.S.[Paulo J.S.], Hashimoto, R.F.[Ronaldo F.], Kim, S.[Seungchan], Barrera, J.[Junior], Brandão, L.O.[Leônidas O.], Suh, E.[Edward], Dougherty, E.R.[Edward R.],
Feature selection algorithms to find strong genes,
PRL(26), No. 10, 15 July 2005, pp. 1444-1453.
Elsevier DOI 0506
BibRef

Peng, H.C.[Han-Chuan], Long, F.H.[Fu-Hui], Ding, C.[Chris],
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy,
PAMI(27), No. 8, August 2005, pp. 1226-1238.
IEEE Abstract. 0506
BibRef

Coetzee, F.M.[Frans M.],
Correcting the Kullback-Leibler distance for feature selection,
PRL(26), No. 11, August 2005, pp. 1675-1683.
Elsevier DOI 0506
BibRef

Brown, M., Costen, N.P.,
Exploratory basis pursuit classification,
PRL(26), No. 12, September 2005, pp. 1907-1915.
Elsevier DOI 0508
BibRef
Earlier:
Non-Linear Feature Selection for Classification,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Luebke, K.[Karsten], Weihs, C.[Claus],
Improving feature extraction by replacing the Fisher criterion by an upper error bound,
PR(38), No. 11, November 2005, pp. 2220-2223.
Elsevier DOI 0509
BibRef

Sima, C.[Chao], Attoor, S.N.[Sanju N.], Braga-Neto, U.M.[Ulisses M.], Lowey, J.[James], Suh, E.[Edward], Dougherty, E.R.[Edward R.],
Impact of error estimation on feature selection,
PR(38), No. 12, December 2005, pp. 2472-2482.
Elsevier DOI 0510
BibRef

Sima, C.[Chao], Dougherty, E.R.[Edward R.],
Optimal convex error estimators for classification,
PR(39), No. 9, September 2006, pp. 1763-1780.
Elsevier DOI
WWW Link. 0606
Bootstrap; Cross-validation; Error estimation; Feature-set ranking; Optimal estimation; Resubstitution; BibRef

Sima, C.[Chao], Dougherty, E.R.[Edward R.],
The peaking phenomenon in the presence of feature-selection,
PRL(29), No. 11, 1 August 2008, pp. 1667-1674.
Elsevier DOI 0804
Classification; Feature-selection; Peaking phenomenon BibRef

Gasca, E., Sánchez, J.S., Alonso, R.,
Eliminating redundancy and irrelevance using a new MLP-based feature selection method,
PR(39), No. 2, February 2006, pp. 313-315.
Elsevier DOI 0512
BibRef

Nanni, L.[Loris],
Cluster-Based Pattern Discrimination: A Novel Technique for Feature Selection,
PRL(27), No. 6, 15 April 2006, pp. 682-687.
Elsevier DOI Feature evaluation and selection; Clustering, Ensemble of classifiers 0604
BibRef

Cord, A.[Aurélien], Ambroise, C.[Christophe], Cocquerez, J.P.[Jean-Pierre],
Feature selection in robust clustering based on Laplace mixture,
PRL(27), No. 6, 15 April 2006, pp. 627-635.
Elsevier DOI Clustering; Feature selection; Laplace distribution; Kruskal-Wallis statistical test; EM algorithm 0604
BibRef

Abe, N.[Naoto], Kudo, M.[Mineichi],
Non-parametric classifier-independent feature selection,
PR(39), No. 5, May 2006, pp. 737-746.
Elsevier DOI 0604
Garbage feature; Non-parametric; Two-stage feature selection Classifier-independent feature selection; Bayes classifier; BibRef

Tenmoto, H.[Hiroshi], Kudo, M.[Mineichi],
Soft Feature Selection by Using a Histogram-Based Classifier,
SSPR08(572-581).
Springer DOI 0812
BibRef

Aoki, K.[Kazuaki], Kudo, M.[Mineichi],
Feature and Classifier Selection in Class Decision Trees,
SSPR08(562-571).
Springer DOI 0812
BibRef

Lee, G.N.[Gobert N.], Bottema, M.J.[Murk J.],
Significance of classification scores subsequent to feature selection,
PRL(27), No. 14, 15 October 2006, pp. 1702-1709.
Elsevier DOI 0609
Multiple comparisons; Statistical significance; Computer-aided diagnosis BibRef

Tang, W.Y.[Wen-Yin], Mao, K.Z.,
Feature selection algorithm for mixed data with both nominal and continuous features,
PRL(28), No. 5, 1 April 2007, pp. 563-571.
Elsevier DOI 0703
Feature selection; Mixed data; Continuous feature; Nominal feature BibRef

Wang, H.Z.[Hong-Zhi], Angelopoulou, E.[Elli],
Sensor band selection for multispectral imaging via average normalized information,
RealTimeIP(1), No. 2, December 2006, pp. 109-121.
Springer DOI 0001
BibRef

Soares de Oliveira, L.E.[Luiz E.], Morita, M.[Marisa], Sabourin, Jr., R.[Robert],
Feature selection for ensembles applied to handwriting recognition,
IJDAR(8), No. 4, September 2006, pp. 262-279.
Springer DOI 0609
See also Impacts of verification on a numeral string recognition system. BibRef

Soares de Oliveira, L.E.[Luiz E.], Sabourin, Jr., R.[Robert], Bortolozzi, F., Suen, C.Y.,
Feature selection for ensembles: A hierarchical multi-objective genetic algorithm approach,
ICDAR03(676-680).
IEEE DOI 0311
BibRef

de-la-Torre, M.[Miguel], Granger, E.[Eric], Sabourin, R.[Robert], Gorodnichy, D.O.[Dmitry O.],
Adaptive skew-sensitive ensembles for face recognition in video surveillance,
PR(48), No. 11, 2015, pp. 3385-3406.
Elsevier DOI 1506
Adaptive classifier ensembles BibRef

de-la-Torre, M.[Miguel], Granger, E.[Eric], Sabourin, R.[Robert], Gorodnichy, D.O.[Dmitry O.],
An adaptive ensemble-based system for face recognition in person re-identification,
MVA(26), No. 6, August 2015, pp. 741-773.
Springer DOI 1508
BibRef

de-la-Torre, M.[Miguel], Granger, E.[Eric], Sabourin, R.[Robert], Gorodnichy, D.O.[Dmitry O.],
Individual-specific management of reference data in adaptive ensembles for face re-identification,
IET-CV(9), No. 5, 2015, pp. 732-740.
DOI Link 1511
face recognition BibRef

Radtke, P.V.W.[Paulo V.W.], Granger, E.[Eric], Sabourin, R.[Robert], Gorodnichy, D.O.[Dmitry O.],
Adaptive selection of ensembles for imbalanced class distributions,
ICPR12(2980-2984).
WWW Link. 1302
BibRef

Radtke, P.V.W., Sabourin, R., Wong, T.[Tong],
Intelligent feature extraction for ensemble of classifiers,
ICDAR05(II: 866-870).
IEEE DOI 0508
BibRef

Radtke, P.V.W., Soares de Oliveira, L.E.[Luiz E.], Sabourin, Jr., R.[Robert], Wong, T.,
Intelligent zoning design using multi-objective evolutionary algorithms,
ICDAR03(824-828).
IEEE DOI 0311
BibRef

Sun, Y.J.[Yi-Jun],
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications,
PAMI(29), No. 6, June 2007, pp. 1035-1051.
IEEE DOI 0704
See also Machine Learning Research: Four Current Directions. BibRef

Guyon, I.[Isabelle], Li, J.W.[Ji-Wen], Mader, T.[Theodor], Pletscher, P.A.[Patrick A.], Schneider, G.[Georg], Uhr, M.[Markus],
Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark,
PRL(28), No. 12, 1 September 2007, pp. 1438-1444.
Elsevier DOI 0707
Feature selection; Matlab; Machine learning; Classification; Challenge; Benchmark; Curriculum BibRef

Deng, Z.H.[Zhao-Hong], Chung, F.L.[Fu-Lai], Wang, S.T.[Shi-Tong],
FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation,
PR(41), No. 4, April 2008, pp. 1363-1372.
Elsevier DOI 0801
Reduced set density estimator; Minimal enclosing ball; Core-set; Data condensation BibRef

Liang, J.N.[Jian-Ning], Yang, S.[Su], Winstanley, A.[Adam],
Invariant optimal feature selection: A distance discriminant and feature ranking based solution,
PR(41), No. 5, May 2008, pp. 1429-1439.
Elsevier DOI 0711
Optimal feature selection; Distance discriminant; Feature ranking BibRef

Yang, S.[Su], Liang, J.N.[Jian-Ning], Wang, Y.Y.[Yuan-Yuan], Winstanley, A.[Adam],
Feature Selection Based on Run Covering,
PSIVT06(208-217).
Springer DOI 0612
BibRef

Akselrod-Ballin, A.[Ayelet], Ullman, S.[Shimon],
Distinctive and compact features,
IVC(26), No. 9, 1 September 2008, pp. 1269-1276.
Elsevier DOI 0806
Feature selection; Object recognition; Distinctive features; Facial features BibRef

Li, B.[Bo], Huang, D.S.[De-Shuang], Wang, C.[Chao], Liu, K.H.[Kun-Hong],
Feature extraction using constrained maximum variance mapping,
PR(41), No. 11, November 2008, pp. 3287-3294.
Elsevier DOI 0808
Multi-manifolds learning; Feature extraction; LDA; MVP; LPP; UDP; Subspace BibRef

Feng, G.Y.[Gui-Yu], Zhang, D.[David], Yang, J.[Jian], Hu, D.[Dewen],
A Theoretical Framework For Matrix-based Feature Extraction Algorithms With Its Application To Image Recognition,
IJIG(8), No. 1, January 2008, pp. 1-23. 0801
BibRef

Ng, W.W.Y.[Wing W.Y.], Yeung, D.S.[Daniel S.], Firth, M.[Michael], Tsang, E.C.C.[Eric C.C.], Wang, X.Z.[Xi-Zhao],
Feature selection using localized generalization error for supervised classification problems using RBFNN,
PR(41), No. 12, December 2008, pp. 3706-3719.
Elsevier DOI 0810
Feature selection; Neural network; Generalization error; RBFNN BibRef

Zeng, H.[Hong], Cheung, Y.M.[Yiu-Ming],
A new feature selection method for Gaussian mixture clustering,
PR(42), No. 2, February 2009, pp. 243-250.
Elsevier DOI 0810
Gaussian mixture; Clustering; Feature selection; Relevance; Redundance BibRef

Zeng, H.[Hong], Cheung, Y.M.[Yiu-Ming],
Feature Selection and Kernel Learning for Local Learning-Based Clustering,
PAMI(33), No. 8, August 2011, pp. 1532-1547.
IEEE DOI 1107
See also Direct Method for Building Sparse Kernel Learning Algorithms, A. LLC Wu and Scholkopf? BibRef

Jia, H.[Hong], Cheung, Y.M.[Yiu-Ming], Liu, J.M.[Ji-Ming],
Cooperative and penalized competitive learning with application to kernel-based clustering,
PR(47), No. 9, 2014, pp. 3060-3069.
Elsevier DOI 1406
Competitive learning BibRef

Paskaleva, B., Hayat, M.M.[Majeed M.], Wang, Z., Tyo, J.S.[J. Scott], Krishna, S.,
Canonical Correlation Feature Selection for Sensors With Overlapping Bands: Theory and Application,
GeoRS(46), No. 10, October 2008, pp. 3346-3358.
IEEE DOI 0810
Specific to issues of overlapping bands. BibRef

Parthalain, N.M.[Neil Mac], Shen, Q.A.[Qi-Ang],
Exploring the boundary region of tolerance rough sets for feature selection,
PR(42), No. 5, May 2009, pp. 655-667.
Elsevier DOI 0902
Feature selection; Attribute reduction; Rough sets; Classification BibRef

Liu, H.W.[Hua-Wen], Sun, J.G.[Ji-Gui], Liuand, L.[Lei], Zhang, H.J.[Hui-Jie],
Feature selection with dynamic mutual information,
PR(42), No. 7, July 2009, pp. 1330-1339.
Elsevier DOI 0903
Classification; Feature selection; Mutual information; Filter method BibRef

Garcia, H.C.[Hugo C.], Villalobos, J.R.[Jesus Rene], Pan, R.[Rong], Runger, G.C.[George C.],
A Novel Feature Selection Methodology for Automated Inspection Systems,
PAMI(31), No. 7, July 2009, pp. 1338-1344.
IEEE DOI 0905
Stepwise variable selection procedure using misclassification error. BibRef

Zhang, W.[Wei], Lin, Z.C.[Zhou-Chen], Tang, X.[Xiaoou],
Tensor linear Laplacian discrimination (TLLD) for feature extraction,
PR(42), No. 9, September 2009, pp. 1941-1948.
Elsevier DOI 0905
Discriminant feature extraction; Tensor; Contextual distance BibRef

Zhao, D.L.[De-Li], Lin, Z.C.[Zhou-Chen], Xiao, R.[Rong], Tang, X.[Xiaoou],
Linear Laplacian Discrimination for Feature Extraction,
CVPR07(1-7).
IEEE DOI 0706
BibRef

Hou, C.Q.[Cui-Qin], Jiao, L.C.[Li-Cheng],
Selecting features of linear-chain conditional random fields via greedy stage-wise algorithms,
PRL(31), No. 2, 15 January 2010, pp. 151-162.
Elsevier DOI 1001
Greedy stage-wise; Feature selection; Linear-chain conditional random fields; Pseudo-likelihood BibRef

Derrac, J.[Joaquín], García, S.[Salvador], Herrera, F.[Francisco],
IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule,
PR(43), No. 6, June 2010, pp. 2082-2105.
Elsevier DOI 1003
BibRef
Earlier:
IFS-CoCo in the Landscape Contest: Description and Results,
ICPR-Contests10(56-65).
Springer DOI 1008
Evolutionary algorithms; Feature selection; Instance selection; Cooperative coevolution; Nearest neighbor BibRef

Zhang, J.C.[Jian-Chun], Zhang, D.Q.[Dao-Qiang],
A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples,
PR(44), No. 6, June 2011, pp. 1162-1171.
Elsevier DOI 1102
Random correlation; Canonical correlation analysis; Feature extraction; Ensemble construction BibRef

Balagani, K.S.[Kiran S.], Phoha, V.V.[Vir V.], Iyengar, S.S., Balakrishnan, N.,
On Guo and Nixon's Criterion for Feature Subset Selection: Assumptions, Implications, and Alternative Options,
SMC-A(40), No. 3, May 2010, pp. 651-655.
IEEE DOI 1003
BibRef

Balagani, K.S.[Kiran S.], Phoha, V.V.[Vir V.],
On the Feature Selection Criterion Based on an Approximation of Multidimensional Mutual Information,
PAMI(32), No. 7, July 2010, pp. 1342-1343.
IEEE DOI 1006
derive the criterion from multidimensional mutual information between features and the calss. Relate to Bayes classification error. BibRef

Garcia-Borroto, M.[Milton], Martinez-Trinidad, J.F.[Jose F.], Carrasco-Ochoa, J.A.[Jesus Ariel], Medina-Perez, M.A.[Miguel Angel], Ruiz-Shulcloper, J.[Jose],
LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification,
PR(43), No. 9, September 2010, pp. 3025-3034.
Elsevier DOI 1006
Discriminative regularities; Emerging patterns; Mixed incomplete data; Comprehensible classifiers BibRef

Medina-Pérez, M.A.[Miguel Angel], García-Borroto, M.[Milton], Ruiz-Shulcloper, J.[José],
Object Selection Based on Subclass Error Correcting for ALVOT,
CIARP07(496-505).
Springer DOI 0711
BibRef

Medina-Pérez, M.A.[Miguel Angel], García-Borroto, M.[Milton], Villuendas-Rey, Y.[Yenny], Ruiz-Shulcloper, J.[José],
Selecting Objects for ALVOT,
CIARP06(606-613).
Springer DOI 0611
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Dornaika, F.[Fadi], El Traboulsi, Y.[Youssof], Assoum, A.,
Inductive and flexible feature extraction for semi-supervised pattern categorization,
PR(60), No. 1, 2016, pp. 275-285.
Elsevier DOI 1609
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Earlier: A1, A2, Only:
A Flexible Semi-supervised Feature Extraction Method for Image Classification,
FSLCV14(III: 122-137).
Springer DOI 1504
Feature extraction BibRef

Dornaika, F.[Fadi], El Traboulsi, Y.[Youssof], Cases, B., Assoum, A.,
Image Classification via Semi-supervised Feature Extraction with Out-of-Sample Extension,
ISVC14(I: 182-192).
Springer DOI 1501
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Li, L.[Liling], Du, L.[Lan], Zhang, W.[Wei], He, H.[Hua], Wang, P.H.[Peng-Hui],
Enhancing information discriminant analysis: Feature extraction with linear statistical model and information-theoretic criteria,
PR(60), No. 1, 2016, pp. 554-570.
Elsevier DOI 1609
Feature transformation BibRef

Ghaemi, M.[Manizheh], Feizi-Derakhshi, M.R.[Mohammad-Reza],
Feature selection using Forest Optimization Algorithm,
PR(60), No. 1, 2016, pp. 121-129.
Elsevier DOI 1609
Feature selection BibRef

Wang, Y.T.[Yin-Tong], Wang, J.D.[Jian-Dong], Liao, H.[Hao], Chen, H.[Haiyan],
An efficient semi-supervised representatives feature selection algorithm based on information theory,
PR(61), No. 1, 2017, pp. 511-523.
Elsevier DOI 1705
Feature selection BibRef

Sheikhpour, R.[Razieh], Sarram, M.A.[Mehdi Agha], Gharaghani, S.[Sajjad], Chahooki, M.A.Z.[Mohammad Ali Zare],
A Survey on semi-supervised feature selection methods,
PR(64), No. 1, 2017, pp. 141-158.
Elsevier DOI 1701
Semi-supervised learning BibRef

Zhang, Z.H.[Zhi-Hong], Tian, Y.Y.[Yi-Yang], Bai, L.[Lu], Xiahou, J.B.[Jian-Bing], Hancock, E.R.[Edwin R.],
High-order covariate interacted Lasso for feature selection,
PRL(87), No. 1, 2017, pp. 139-146.
Elsevier DOI 1703
Lasso BibRef

Das, A.[Ayan], Das, S.[Swagatam],
Feature weighting and selection with a Pareto-optimal trade-off between relevancy and redundancy,
PRL(88), No. 1, 2017, pp. 12-19.
Elsevier DOI 1703
Feature selection BibRef

Senawi, A.[Azlyna], Wei, H.L.[Hua-Liang], Billings, S.A.[Stephen A.],
A new maximum relevance-minimum multicollinearity (MRmMC) method for feature selection and ranking,
PR(67), No. 1, 2017, pp. 47-61.
Elsevier DOI 1704
Dimensionality reduction BibRef

Li, F.[Feng], Miao, D.[Duoqian], Pedrycz, W.[Witold],
Granular multi-label feature selection based on mutual information,
PR(67), No. 1, 2017, pp. 410-423.
Elsevier DOI 1704
Granular computing BibRef

Kifah, S.[Saif], Abdullah, S.[Salwani], Arajy, Y.Z.[Yahya Z.],
Solving feature selection problem using intelligent double treatment iterative composite neighbourhood structure algorithm,
IJCVR(7), No. 3, 2017, pp. 255-275.
DOI Link 1704
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Jia, Y., Ma, J., Gan, L.,
Combined Optimization of Feature Reduction and Classification for Radiometric Identification,
SPLetters(24), No. 5, May 2017, pp. 584-588.
IEEE DOI 1704
feature extraction BibRef

Liu, C.[Chuan], Wang, W.Y.[Wen-Yong], Zhao, Q.A.[Qi-Ang], Shen, X.M.[Xiao-Ming], Konan, M.[Martin],
A new feature selection method based on a validity index of feature subset,
PRL(92), No. 1, 2017, pp. 1-8.
Elsevier DOI 1705
Feature selection. BibRef

Yuan, M.S.[Ming-Shun], Yang, Z.J.[Zi-Jiang], Huang, G.Z.[Guang-Zao], Ji, G.[Guoli],
Feature selection by maximizing correlation information for integrated high-dimensional protein data,
PRL(92), No. 1, 2017, pp. 17-24.
Elsevier DOI 1705
Feature, selection 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

Yang, B.[Bin], Lu, Y.L.[Yu-Liang], Zhu, K.L.[Kai-Long], Yang, G.Z.[Guo-Zheng], Liu, J.W.[Jing-Wei], Yin, H.B.[Hai-Bo],
Feature Selection Based on Modified Bat Algorithm,
IEICE(E100-D), No. 8, August 2017, pp. 1860-1869.
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Xie, G.S.[Guo-Sen], Zhang, X.Y.[Xu-Yao], Yan, S.C.[Shui-Cheng], Liu, C.L.[Cheng-Lin],
SDE: A Novel Selective, Discriminative and Equalizing Feature Representation for Visual Recognition,
IJCV(124), No. 2, September 2017, pp. 145-168.
Springer DOI 1708
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Xie, G.S.[Guo-Sen], Zhang, X.Y.[Xu-Yao], Shu, X.B.[Xiang-Bo], Yan, S.C.[Shui-Cheng], Liu, C.L.[Cheng-Lin],
Task-Driven Feature Pooling for Image Classification,
ICCV15(1179-1187)
IEEE DOI 1602
Encoding BibRef

Prasad, Y.[Yamuna], Khandelwal, D.[Dinesh], Biswas, K.K.,
Max-Margin feature selection,
PRL(95), No. 1, 2017, pp. 51-57.
Elsevier DOI 1708
Feature selection BibRef

Chen, S., Yang, J., Luo, L., Wei, Y., Zhang, K., Tai, Y.,
Low-Rank Latent Pattern Approximation With Applications to Robust Image Classification,
IP(26), No. 11, November 2017, pp. 5519-5530.
IEEE DOI 1709
Feature extraction, Image reconstruction, Lighting, Measurement, Robustness, Testing, Training, ADMM, 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


Yassine, A., Mohamed, C., Zinedine, A.,
Feature selection based on pairwise evalution,
ISCV17(1-6)
IEEE DOI 1710
decision trees, feature selection, AUC, area under the ROC curve, decision tree algorithm, hybrid filter-wrapper algorithm, pairwise evaluation, pairwise feature selection, BibRef

Rodríguez-Diez, V.[Vladímir], Martínez-Trinidad, J.F.[José F.], Carrasco-Ochoa, J.A.[J. Ariel], Lazo-Cortés, M.S.[Manuel S.],
Fast-BR vs. Fast-CT_EXT: An Empirical Performance Study,
MCPR17(127-136).
Springer DOI 1706
feature selection in supervised classification BibRef

Cui, L.X.[Li-Xin], Jiao, Y.H.[Yu-Hang], Bai, L.[Lu], Rossi, L.[Luca], Hancock, E.R.[Edwin R.],
Adaptive Feature Selection Based on the Most Informative Graph-Based Features,
GbRPR17(276-287).
Springer DOI 1706
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Sharmin, S., Ali, A.A., Khan, M.A.H., Shoyaib, M.,
Feature Selection and Discretization based on Mutual Information,
IVPR17(1-6)
IEEE DOI 1704
Classification algorithms 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
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Kourid, A., Batouche, M.,
A novel approach for feature selection based on MapReduce for biomarker discovery,
ICCVIA15(1-11)
IEEE DOI 1603
Big Data BibRef

Gao, T., Wang, Z., Ji, Q.,
Structured Feature Selection,
ICCV15(4256-4264)
IEEE DOI 1602
Approximation algorithms BibRef

Roffo, G., Melzi, S., Cristani, M.,
Infinite Feature Selection,
ICCV15(4202-4210)
IEEE DOI 1602
Benchmark testing BibRef

Foroughi, H.[Homa], Shakeri, M.[Moein], Ray, N.[Nilanjan], Zhang, H.[Hong],
Joint Feature Selection with Low-rank Dictionary Learning,
BMVC15(xx-yy).
DOI Link 1601
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Liu, C.[Chao], Skaff, S.[Sandra], Martinello, M.[Manuel],
Learning Discriminative Spectral Bands for Material Classification,
ISVC15(I: 671-681).
Springer DOI 1601
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Liu, L.J.[Li-Juan], Bao, Y.[Yu], Li, H.J.[Hao-Jie], Fan, X.[Xin], Luo, Z.X.[Zhong-Xuan],
Discriminative Feature Learning with an Optimal Pattern Model for Image Classification,
MMMod16(I: 675-685).
Springer DOI 1601
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Alhutaish, R.[Roiss], Omar, N.[Nazlia], Abdullah, S.[Salwani],
A Comparison of Multi-label Feature Selection Methods Using the Algorithm Adaptation Approach,
IVIC15(199-212).
Springer DOI 1511
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Vicente, F.F.R.[Fábio F. R.], Menezes, E.[Euler], Rubino, G.[Gabriel], de Oliveira, J.[Juliana], Lopes, F.M.[Fabrício Martins],
A Feature Selection Approach for Evaluate the Inference of GRNs Through Biological Data Integration - A Case Study on A. Thaliana,
CIARP15(667-675).
Springer DOI 1511
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Eriksson, A.[Anders], Pham, T.T.[Trung Thanh], Chin, T.J.[Tat-Jun], Reid, I.D.[Ian D.],
The k-support norm and convex envelopes of cardinality and rank,
CVPR15(3349-3357)
IEEE DOI 1510
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Ferreira, A.J.[Artur J.], Figueiredo, M.A.T.[Mário A. T.],
Exploiting the Bin-Class Histograms for Feature Selection on Discrete Data,
IbPRIA15(345-353).
Springer DOI 1506
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Xie, G.S.[Guo-Sen], Zhang, X.Y.[Xu-Yao], Liu, C.L.[Cheng-Lin],
Efficient Feature Coding Based on Auto-encoder Network for Image Classification,
ACCV14(I: 628-642).
Springer DOI 1504
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Fu, J.L.[Jian-Long], Wang, J.Q.[Jin-Qiao], Wang, X.J.[Xin-Jing], Rui, Y.[Yong], Lu, H.Q.[Han-Qing],
What Visual Attributes Characterize an Object Class?,
ACCV14(I: 243-259).
Springer DOI 1504
Much more than feature selection for classification, higher level attributes. BibRef

Macák, J.[Jan], Drbohlav, O.[Ondrej],
A Simple Stochastic Algorithm for Structural Features Learning,
FSLCV14(III: 44-55).
Springer DOI 1504
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Sarkar, R.[Rituparna], Skadron, K.[Kevin], Acton, S.T.[Scott T.],
A meta-algorithm for classification by feature nomination,
ICIP14(5187-5191)
IEEE DOI 1502
Accuracy 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

Rodrigues, D.[Douglas], Pereira, L.A.M.[Luis A.M.], Papa, J.P.[Joao P.], Weber, S.A.T.[Silke A.T.],
A Binary Krill Herd Approach for Feature Selection,
ICPR14(1407-1412)
IEEE DOI 1412
Accuracy BibRef

Huang, S.R.[Shang-Rong], Zhang, J.[Jian], Liu, X.W.[Xin-Wang], Wang, L.[Lei],
A Method of Discriminative Information Preservation and In-Dimension Distance Minimization Method for Feature Selection,
ICPR14(1615-1620)
IEEE DOI 1412
Accuracy BibRef

Touazi, A.[Azzedine], Mokdad, F.[Fatiha], Bouchaffra, D.[Djamel],
Feature Selection Scheme Based on Zero-Sum Two-Player Game,
ICPR14(1342-1347)
IEEE DOI 1412
Accuracy BibRef

Chen, L.[Lin], Zhang, Q.A.[Qi-Ang], Li, B.X.[Bao-Xin],
Predicting Multiple Attributes via Relative Multi-task Learning,
CVPR14(1027-1034)
IEEE DOI 1409
learn ranking functions describing the relative strength of attributes. BibRef

Li, Z.W.[Zhu-Wen], Yang, S.G.[Shuo-Guang], Cheong, L.F.[Loong-Fah], Toh, K.C.[Kim-Chuan],
Simultaneous Clustering and Model Selection for Tensor Affinities,
CVPR16(5347-5355)
IEEE DOI 1612
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Li, Z.W.[Zhu-Wen], Cheong, L.F.[Loong-Fah], Zhou, S.Z.[Steven Zhiying],
SCAMS: Simultaneous Clustering and Model Selection,
CVPR14(264-271)
IEEE DOI 1409
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Kärkkäinen, T.[Tommi],
On Cross-Validation for MLP Model Evaluation,
SSSPR14(291-300).
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Liu, Y.L.[Ying-Lu], Hou, X.W.[Xin-Wen], Liu, C.L.[Cheng-Lin],
A Compact Spatial Feature Representation for Image Classification,
ACPR13(601-605)
IEEE DOI 1408
feature extraction BibRef

Mochizuki, T., Sumiyoshi, H., Sano, M., Fujii, M.,
Visual-Based Image Retrieval by Block Reallocation Considering Object Region,
ACPR13(371-375)
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feature extraction BibRef

Rodriguez, P.A.[Pedro A.], Drenkow, N.[Nathan], DeMenthon, D.F.[Daniel F.], Koterba, Z.[Zachary], Kauffman, K.[Kathleen], Cornish, D.[Duane], Paulhamus, B.[Bart], Vogelstein, R.J.[R. Jacob],
Selection of universal features for image classification,
WACV14(355-362)
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Feature extraction BibRef

Gilani, S.Z.[Syed Zulqarnain], Shafait, F.[Faisal], Mian, A.[Ajmal],
Gradient based efficient feature selection,
WACV14(191-197)
IEEE DOI 1406
Accuracy BibRef

Xin, X.[Xin], Li, Z.[Zhu], Ma, Z.[Zhan], Katsaggelos, A.K.[Aggelos K.],
Robust feature selection with self-matching score,
ICIP13(4363-4366)
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compact visual descriptor;mobile visual search;self-matching score BibRef

Ishii, M.[Masato], Sato, A.[Atsushi],
Feature selection using graph cuts based on relevance and redundancy,
ICIP13(4292-4296)
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Machine learning; feature selction; graph cut; submodular function BibRef

Duarte, J.M.M.[João M.M.], Fred, A.L.N.[Ana L.N.], Duarte, F.J.F.[Fernando Jorge F.],
A Constraint Acquisition Method for Data Clustering,
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Optimizing Feature Selection through Binary Charged System Search,
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Nested Dichotomies Based on Clustering,
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Gonçalves, N.[Nicolau], Vigário, R.[Ricardo],
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Carmona, P.L.[Pedro Latorre], Martínez Sotoca, J.[José], Pla, F.[Filiberto], Phoa, F.K.H.[Frederick K.H.], Bioucas-Dias, J.M.[José M.],
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Feature Selection Using Multiobjective Optimization for Named Entity Recognition,
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Quantum fuzzy particle swarm optimization algorithm for image clustering,
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Feature Selection Based on Mutual Information for Language Recognition,
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Image Classification from Small Sample, with Distance Learning and Feature Selection,
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Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion,
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IEEE DOI 0609
BibRef

Frintrop, S.[Simone], Jensfelt, P.[Patric], Christensen, H.I.[Henrik I.],
Pay Attention When Selecting Features,
ICPR06(II: 163-166).
IEEE DOI 0609
BibRef

Krížek, P.[Pavel], Kittler, J.V.[Josef V.], Hlavác, V.[Václav],
Improving Stability of Feature Selection Methods,
CAIP07(929-936).
Springer DOI 0708
BibRef

Krizek, P.[Pavel], Kittler, J.V.[Josef V.], Hlavac, V.[Vaclav],
Feature condensing algorithm for feature selection,
ICPR08(1-4).
IEEE DOI 0812
BibRef
Earlier:
Feature selection based on the training set manipulation,
ICPR06(II: 658-661).
IEEE DOI 0609
BibRef

Franceschi, E., Odone, F., Smeraldi, F., Verri, A.,
Feature Selection with Nonparametric Statistics,
ICIP05(I: 325-328).
IEEE DOI 0512
BibRef

Yoon, S.H.[Sang-Ho], Gray, R.M.,
Feature Selection Based on Maximizing Separability in Gauss Mixture Model and its Application to Image Classification,
ICIP05(II: 1198-1201).
IEEE DOI 0512
BibRef

Le Saux, B.[Bertrand], Bunke, H.[Horst],
Feature Selection for Graph-Based Image Classifiers,
IbPRIA05(II:147).
Springer DOI 0509
BibRef

Binaghi, E.[Elisabetta], Gallo, I.[Ignazio], Boschetti, M.[Mirco], Brivio, P.A.[P. Alessandro],
A Neural Adaptive Algorithm for Feature Selection and Classification of High Dimensionality Data,
CIAP05(753-760).
Springer DOI 0509
BibRef

Xu, Q.R.[Qian-Ren], Kamel, M., Salama, M.M.A.,
Significance Test for Feature Subset Selection on Image Recognition,
ICIAR04(I: 244-252).
Springer DOI 0409
BibRef

Hu, J.Y.[Jian-Ying], Ratzlaff, E.,
Probability table compression using distributional clustering for scanning N-tuple classifiers,
ICPR04(II: 533-536).
IEEE DOI 0409
BibRef

Markou, M., Singh, S.,
Feature selection based on a black hole model of data reorganization,
ICPR04(IV: 565-568).
IEEE DOI 0409
BibRef

Singh, S., Singh, M., Markou, M.,
Feature selection for face recognition based on data partitioning,
ICPR02(I: 680-683).
IEEE DOI 0211
BibRef

Farmer, M.E., Bapna, S., Jain, A.K.,
Large scale feature selection using modified random mutation hill climbing,
ICPR04(II: 287-290).
IEEE DOI 0409
BibRef

Washizawa, Y.[Yoshikazu],
Trace Norm Regularization and Application to Tensor Based Feature Extraction,
Subspace10(404-413).
Springer DOI 1109
BibRef

Washizawa, Y.[Yoshikazu], Tanaka, M.[Masayuki],
Centered Subset Kernel PCA for Denoising,
Subspace10(354-363).
Springer DOI 1109
BibRef

Washizawa, Y.[Yoshikazu],
Subset kernel PCA for pattern recognition,
Subspace09(162-169).
IEEE DOI 0910
BibRef

Washizawa, Y.[Yoshikazu], Yamashita, Y.[Yukihiko],
Non-linear Wiener filter in reproducing kernel Hilbert space,
ICPR06(I: 967-970).
IEEE DOI 0609
BibRef
Earlier:
Kernel sample space projection classifier for pattern recognition,
ICPR04(II: 435-438).
IEEE DOI 0409
BibRef

Wu, Y.M.[Yi-Min], Zhang, A.D.[Ai-Dong],
Feature selection for classifying high-dimensional numerical data,
CVPR04(II: 251-258).
IEEE DOI 0408
BibRef

Li, J.M.[Jian-Min], Mang, B.[Bo], Lin, F.[Fuzong],
A new strategy for selecting working sets applied in SMO,
ICPR02(III: 427-430).
IEEE DOI 0211
BibRef

Torkkola, K.,
Learning feature transforms is an easier problem than feature selection,
ICPR02(II: 104-107).
IEEE DOI 0211
BibRef

Al-Ani, A., Deriche, M.,
Feature selection using a mutual information based measure,
ICPR02(IV: 82-85).
IEEE DOI 0211
BibRef

Vasconcelos, N.M.[Nuno M.], Carneiro, G.[Gustavo],
What Is the Role of Independence for Visual Recognition?,
ECCV02(I: 297 ff.).
Springer DOI 0205
BibRef

Onnia, V., Tico, M., Saarinen, J.,
Feature Selection Method Using Neural Network,
ICIP01(I: 513-516).
IEEE DOI 0108
BibRef

Bins, J.[Jose], Draper, B.A.[Bruce A.],
Feature Selection from Huge Feature Sets,
ICCV01(II: 159-165).
IEEE DOI 0106
BibRef

Smits, P.C., Annoni, A.,
Cost-based Feature Subset Selection for Interactive Image Analysis,
ICPR00(Vol II: 386-389).
IEEE DOI 0009
BibRef

Holz, H.J., Loew, M.H.,
Validation of Relative Feature Importance Using a Natural Data Set,
ICPR00(Vol II: 414-417).
IEEE DOI 0009
BibRef

Baesens, B., Viaene, S., Vanthienen, J., Dedene, G.,
Wrapped Feature Selection by Means of Guided Neural Network Optimisation,
ICPR00(Vol II: 113-116).
IEEE DOI 0009
BibRef

Kawatani, T., Shimizu, H.,
Complementary Classifier Design Using Difference Principal Components,
ICDAR97(875-880).
IEEE DOI 9708
BibRef

Xuan, G., Peiqi, C., Minhui, W.,
Bhattacharyya Distance Feature Selection,
ICPR96(II: 195-199).
IEEE DOI 9608
(Tongji Univ., PRC) BibRef

Yamakawa, H.,
Matchability Oriented Feature Selection for Recognition Structure Learning,
ICPR96(IV: 123-127).
IEEE DOI 9608
(Real World Computing Partners., J) BibRef

Kuncheva, L.I.[Ludmila I.], Kounchev, R.K.[Roumen K.],
On feature selection via rough sets,
CAIP95(625-630).
Springer DOI 9509
BibRef

Mao, J.C.[Jian-Chang], Mohiuddin, K.M., Jain, A.K.,
Parsimonious network design and feature selection through node pruning,
ICPR94(B:622-624).
IEEE DOI 9410
BibRef

Raudys, S.J.[Sarunas J.],
Accuracy of feature selection and extraction in statistical and neural net pattern classification,
ICPR92(II:62-70).
IEEE DOI 9208
BibRef
Earlier:
On the accuracy of a bootstrap estimate of the classification error,
ICPR88(II: 1230-1232).
IEEE DOI 8811
BibRef

Kira, K., and Rendell, L.A.,
A Practical Approach to Feature Selection,
ConferenceNinth Conference on Machine Learning, 1992, pp. 249-256. RELIEF Algorithm See also Machine Learning Research: Four Current Directions. BibRef 9200

Sheinvald, J., Dom, B., Niblack, W.,
A modeling approach to feature selection,
ICPR90(I: 535-539 vol).
IEEE DOI 9006
BibRef

Chen, M.H., Lee, D., Pavlidis, T.,
Some results on feature detection using residual analysis,
ICPR90(I: 668-670).
IEEE DOI 9006
BibRef

Dom, B., Niblack, W., Sheinvald, J.,
Feature selection with stochastic complexity,
CVPR89(241-248).
IEEE DOI 0403
BibRef

Blanz, W.E.,
Nonparametric feature selection for multiple class processes,
ICPR88(II: 1032-1035).
IEEE DOI 8811
BibRef

Selkainaho, K., Pakkinen, J.,
ICC statistic as criterion for classification and feature selection,
ICPR88(II: 709-711).
IEEE DOI 8811
BibRef

Xu, L.[Lei], Yan, P.F.[Ping-Fan], Chang, T.[Tong],
Best first strategy for feature selection,
ICPR88(II: 706-708).
IEEE DOI 8811
BibRef

Bobrowski, L.,
Feature selection based on some homogeneity coefficient,
ICPR88(I: 544-546).
IEEE DOI 8811
BibRef

Segen, J.,
Clumping with feature selection and Occam's razor,
ICPR88(I: 541-543).
IEEE DOI 8811
BibRef

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


Last update:Nov 11, 2017 at 13:31:57