Unsupervised feature selection using weighted principal components

Expert Systems with Applications

Volumn 38, Issue 5, 2011, Pages 5704-5710

  Kim, Seoung Bum ^a   Rattakorn, Panaya ^b  

^a Korea University   (South Korea)

^b The University of Texas at Arlington   (United States)

Author keywords

Data mining; Feature selection; Principal component analysis; Unsupervised learning

Indexed keywords

DIMENSIONAL REDUCTION; FEATURE SELECTION; INTERPRETABILITY; LINEAR COMBINATIONS; PRINCIPAL COMPONENTS; REAL DATA SETS; STATISTICAL MODELS; THRESHOLDING ALGORITHMS; UNSUPERVISED FEATURE SELECTION; WEIGHTED SUM;

ALGORITHMS; DATA MINING; FEATURE EXTRACTION; LOADING; MICROCOMPUTERS; UNSUPERVISED LEARNING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 79151480230     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.10.063     Document Type: Article

References (18)

1. D.G. Altman, and J.M. Bland Diagnostic tests. 1: Sensitivity and specificity BMJ 308 1994 1552
2. H.-W. Cho, S.B. Kim, M. Jeong, Y. Park, T.R. Ziegler, and D.P. Jones Genetic algorithm-based feature selection in high-resolution NMR spectra Expert Systems with Applications 35 2008 967 975
3. R.A. Davis, A.J. Charlton, S. Oehlschlager, and J.C. Wilson Novel feature selection method for genetic programming using metabolomic H1 NMR data Chemometrics and Intelligent Laboratory Systems 81 2006 50 59
4. C. Ding Unsupervised feature selection via two-way ordering in gene expression analysis Bioinformatics 19 10 2003 1259 1266
5. Dy, J. & Brodley, C. (2000). Feature subset selection and order identification for unsupervised learning. In Proceedings of the 17th international conference on machine learning (pp. 247-254).
6. I. Guyon, and A. Elisseeff An introduction to variable and feature selection Journal of Machine Learning Research 3 2003 1157 1182
7. Hastie, T.; Tibshirani, R.; Eisen, M.; Alizadeh, A.; Levy, R.; Staudt, L.; et al. (2000). 'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology, 1(2), RESEARCH0003.
8. A.K. Jain, R.P.W. Duin, and J. Mao Statistical pattern recognition: A review IEEE Transactions on Pattern Analysis & Machine Intelligence 20 2000 4 37
9. R.A. Johnson, and D.W. Wichern Applied multivariate statistical analysis fifth ed. 2001 Prentice-Hall, Inc. New Jersey
10. I.T. Jolliffe Principal component analysis 2002 Springer-Verlag New York
11. S.B. Kim Features extraction and selection in high-dimensional spectral data J. Wang, Encyclopedia of data warehousing and mining second ed. 2008 IGI Global Pennsylvania 863 869
12. S.B. Kim, V.C.P. Chen, Y. Park, T.R. Ziegler, and D.P. Jones Controlling the false discovery rate for feature selection in high-resolution NMR spectra Statistical Analysis and Data Mining 1 2008 57 66
13. Kim, Y.; & Gao, J. (2006). Unsupervised gene selection for high dimensional data. In Proceedings of IEEE symposium of bioinformatics and bioengineering (IEEE BIBE) (pp. 227-232).
14. K.Z. Mao Identifying critical variables of principal components for unsupervised feature selection IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 35 2 2005 339 344
15. Y. Mei, S.B. Kim, and K.-L. Tsui Identification of major metabolite features in high-resolution NMR spectra using linear-mixed effects models Expert Systems with Applications 36 2009 4703 4708
16. J. Shawe-Taylor, and N. Cristianini Support vector machines and other kernel-based learning methods 2000 Cambridge University New York
17. M.B. Vermaat, R.A. Ion, R.J.M.M. Does, and C.A.J. Klaassen A comparison of Shewhart individuals control charts based on normal, non-parametric, and extreme-value theory Quality and Reliability Engineering International 19 2003 337 353
18. W.H. Woodall, and D.C. Montgomery Research issues and ideas in statistical process control Journal of Quality Technology 31 1999 376 386