Feature extraction based on subspace methods for regression problems

Neurocomputing

Volumn 73, Issue 10-12, 2010, Pages 1740-1751

  Kwak, Nojun ^a   Lee, Jung Won ^a  

^a Ajou University   (South Korea)

Author keywords

Dimensionality reduction; Feature extraction; LDA; PCA; Regression; Subspace method

Indexed keywords

DIMENSIONALITY REDUCTION; FEATURE EXTRACTION METHODS; LINEAR DISCRIMINANT ANALYSIS; LINEAR TRANSFORM; OPTIMIZATION PROCESS; PRINCIPLE COMPONENT ANALYSIS; REGRESSION PROBLEM; SUB-SPACE METHODS; TARGET INFORMATION; TARGET VALUES;

DISCRIMINANT ANALYSIS; REGRESSION ANALYSIS;

FEATURE EXTRACTION;

ARTICLE; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; DATA EXTRACTION; DISCRIMINANT ANALYSIS; INDEPENDENT COMPONENT ANALYSIS; INTERMETHOD COMPARISON; K NEAREST NEIGHBOR; KERNEL METHOD; LINEAR REGRESSION ANALYSIS; MATHEMATICAL COMPUTING; NONLINEAR REGRESSION ANALYSIS; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REGRESSION ANALYSIS; SENSITIVITY ANALYSIS; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 77952584207     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.10.025     Document Type: Article

References (31)

1. Devijver P., Kittler J. Pattern Recognition: A Statistical Approach 1982, Prentice Hall, London.
2. Cios K., Pedrycz W., Swiniarski R. Data Mining Methods for Knowledge Discovery 1998, Kluwer Academic Publishers, (Chapter 9).
3. Joliffe I. Principal Component Analysis 1986, Springer-Verlag.
4. Bell A., Sejnowski T. An information-maximization approach to blind separation and blind deconvolution. Neural Computation 1995, 7(6):1129-1159.
5. Fukunaga K. Introduction to Statistical Pattern Recognition 1990, Academic Press. second ed.
6. D. Cai, X. He, J. Han, Spectral regression: a unified subspace learning framework for content-based image retrieval, in: ACM Multimedia, 2007.
7. Okada T., Tomita S. An optimal orthonormal system for discriminant analysis. Pattern Recognition 1985, 18(2):139-144.
8. Loog M., Duin R. Linear dimensionality reduction via a heteroscedastic extension of lda: The Chernoff criterion. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26(6):732-739.
9. D. Zhou, X. Yang, Face recognition using direct-weighted lda, in: Lecture Notes in Computer Science, vol. 3157, 2004, pp. 760-768.
10. Tao D., Li X., Wu X., Maybank S. Geometric mean for subspace selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009, 31(2):260-274.
11. Kwak N., Choi C.-H. Feature extraction based on ica for binary classification problems. IEEE Transactions on Knowledge and Data Engineering 2003, 15(6):1374-1388.
12. J. Goldberger, S. Roweis, G. Hinton, R. Salakhutdinov, Neighborhood components analysis, NIPS. URL: 〈〉. http://www.cs.utoronto.ca/~roweis/papers/ncanips.pdf.
13. Tao D., Li X., Hu W., Maybank S., Wu X. Supervised tensor learning. Knowledge and Information Systems 2007, 13(1):1-42.
14. Tao D., Li X., Wu X., Maybank S. General tensor discriminant analysis and Gabor features for gait recognition. IEEE Transactions on Pattern Recognition and Machine Intelligence 2007, 29(10):1700-1715.
15. Li X., Lin S., Yan S., Xu D. Discriminant locally linear embedding with high order tensor data. IEEE Transactions on Systems, Man and Cybernetics-Part B 2008, 38(2):342-352.
16. C. Rasmussen, C. Williams, Gaussian Processes for Machine Learning, first ed, The MIT Press, 2006.
17. Weisberg S. Applied Linear Regression 1985, John Wiley, New York, p. 324 (Chapter 3). second ed.
18. Loog M. Supervised Dimensionality Reduction and Contextual Pattern Recognition in Medical Image Processing 2004, Ponsen & Looijen, Wageningen, The Netherlands, (Chapter 3).
19. Li K.C. Sliced inverse regression for dimension reduction (with discussion). Journal of the American Statistical Association 1991, 86:316-342.
20. Li K.C. On principal Hessian directions for data visualization and dimension reduction: another application of Stein's lemma. Journal of the American Statistical Association 1992, 87:1025-1039.
21. N. Kwak, C. Kim, Dimensionality reduction based on ica for regression problems, in: Proceedings of the International Conference on Artificial Neural Networks, 2006, pp. 1-10.
22. Stein C. Estimation of the mean of a multivariate normal distribution. The Annals of Statistics 1981, 9:1135-1151.
23. J. Ye, Least squares linear discriminant analysis, in: Proceedings of the 24th International Conference on Machine Learning, 2007, pp. 1087-1093.
24. Lu J., Plataniotis K., Venetsanopoulos A. Regularization studies of linear discriminant analysis in small sample size scenarios with applications to face recognition. Pattern Recognition Letters 2005, 26:181-191.
25. Yang J., Frangi F., Yang J.-Y., Zhang D., Jin Z. Kpca plus lda: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 2005, 27(2):230-244.
26. X.S. Zhou, T.S. Huang, Small sample learning during multimedia retrieval using biasmap, in: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, vol. 1, Hawaii, USA, 2001, pp. 11-17.
27. Friedman J. Regularized discriminant analysis. Journal of American Statistics Association 1989, 84:165-175.
28. Narayada E. On estimating regression. Theory of Probability and its Applications 1964, 9:141-142.
29. D. Newman, S. Hettich, C. Blake, C. Merz, UCI repository of machine learning databases 〈〉, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
30. M. Meurens, Orange juice data. http://www.dice.ucl.ac.be/mlg/DataBases/ORANGE_JUICE/.
31. Vijayakumar S., D'Souza A., Schaal S. Incremental online learning in high dimensions. Neural Computation 2005, 17(12):2602-2634.