Kernel-view based discriminant approach for embedded feature extraction in high-dimensional space

Neurocomputing

Volumn 74, Issue 9, 2011, Pages 1478-1484

  Cheng, Miao ^a,b   Fang, Bin ^a   Pun, Chi Man ^b   Yan Tang, Yuan ^a,b  

^a CHONGQING UNIVERSITY   (China)

^b UNIVERSITY OF MACAU   (Macao)

Author keywords

Curse of dimensionality; Dimensionality reduction; Feature extraction; Kernel analysis; Local discriminant analysis

Indexed keywords

CLUSTERING ALGORITHMS; DISCRIMINANT ANALYSIS; EXTRACTION; FEATURE EXTRACTION; LEARNING ALGORITHMS;

CURSE OF DIMENSIONALITY; DIMENSIONALITY REDUCTION; DISCRIMINANT EMBEDDING; HIGH DIMENSIONAL DATA; HIGH DIMENSIONAL SPACES; KERNEL ANALYSIS; LOCAL DISCRIMINANT ANALYSIS; MANIFOLD LEARNING ALGORITHM;

DATA MINING;

ARTICLE; CONTROLLED STUDY; DATA EXTRACTION; DATA MINING; DISCRIMINANT ANALYSIS; DISCRIMINATION LEARNING; INTERMETHOD COMPARISON; KERNEL METHOD; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MULTIDIMENSIONAL SCALING; PATTERN RECOGNITION; PRIORITY JOURNAL;

EID: 79953044619     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.01.004     Document Type: Article

References (33)

1. Turk M., Pentland A. Eigenfaces for recognition. J. Cogn. Neurosci. 1991, 3(1):71-86.
2. Fukunaga K. Introduction to Statistical Pattern Recognition 1990, Academic Press.
3. Belhumeur P., Hespanha J., Kriegman D. Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 1997, 19(7):711-720.
4. Schölkopf B., Smola A., Müller K.R. Nonlinear component analysis as a kernel Eigenvalue problem. Neural Comput. 1998, 10:1299-1319.
5. S. Mika, G. Ratsch, J. Weston, B. Schölkopf, K. Muller, Fisher discriminant analysis with kernels, in: Proceedings of the IEEE International Workshop on Neural Networks for Signal Processing, 1999, pp. 41-48.
6. Billings S.A., Lee K.L. Nonlinear fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm. Neural Networks 2002, 15(2):263-270.
7. Harrison R.F., Pasupa K. Sparse multinomial kernel discriminant analysis (sMKDA). Pattern Recognition 2009, 42(9):1795-1802.
8. Tenenbaum J., Silva V., Langford J. A global geometric framework for nonlinear dimensionality reduction. Science 2000, 290(5500):2319-2323.
9. Roweis S., Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290(5500):2323-2326.
10. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Neural Information Processing Systems, 2001, pp. 585-591.
11. He X., Yan S., Hu Y., Niyogi P., Zhang H. Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(3):328-340.
12. Yan S., Xu D., Zhang B.Y., Zhang H.J., Yang Q., Lin S. Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(1):40-51.
13. Li X.L., Lin S., Yan S.C., Xu D. Discriminant locally linear embedding with high-order tensor data. IEEE Trans Syst. Man Cybern. Part B: Cybern. 2008, 38(2):342-352.
14. Fu Y., Yan S., Huang T.S. Classification and feature extraction by simplexization. IEEE Trans. Inf. Forens. Secur. 2008, 3(1):91-100.
15. Cheng M., Fang B., Wen J., Tang Y.Y. Marginal discriminant projections: an adaptable marginal discriminant approach to feature reduction and extraction. Pattern Recognition Lett. 2010, 31(13):1965-1974.
16. Cheng M., Fang B., Tang Y.Y., Zhang T., Wen J. Incremental embedding and learning in the local discriminant subspace with application to face recognition. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 2010, 40(5):580-591.
17. Li H.F., Jiang T., Zhang K. Efficient and robust feature extraction by maximum margin criterion. IEEE Trans. Neural Networks 2006, 17(1):157-165.
18. A. Asuncion, D. Newman, UCI machine learning repository, 〈〉, 2007. http://www.ics.uci.edu/~mlearn/MLRepository.html.
19. Yang J., Yang J.y. Why can LDA be performed in PCA transformed space?. Pattern Recognition 2003, 36(2):563-566.
20. Ham J., Lee D., Mika S., Schölkopf B. A kernel view of the dimensionality reduction of manifolds. Proceedings of the 21st International Conference on Machine Learning 2004, vol. 69:369-376.
21. Baudat G., Anouar F. Generalized discriminant analysis using a kernel approach. Neural Comput. 2000, 12(10):2385-2404.
22. G.H. Golub, C.F.V. Loan, Matrix Computations, third ed., Johns Hopkins University Press, Baltimore, MD, 1996.
23. Ye J. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. J. Mach. Learn. Res. 2005, 6:483-502.
24. Yu H., Yang J. A direct LDA algorithm for high dimensional data-with application to face recognition. Pattern Recognition 2001, 34(10):2067-2070.
25. Ji S., Ye J. Generalized linear discriminant analysis: a unified framework and efficient model selection. IEEE Trans. Neural Networks 2008, 19(10):1768-1782.
26. Cai D., He X., Zhou K., Han J., Bao H. Locality sensitive discriminant analysis. Proceedings of the International Joint Conference on Artificial Intelligence 2007, 708-713.
27. Fang B., Cheng M., Tang Y.Y., He G. Improving the discriminant ability of local margin based learning method by incorporating the global between-class separability criterion. Neurocomputing 2009, 73(1-3):356-541.
28. Su A.I., et al. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res. 2001, 61(20):7388-7393.
29. Ramaswamy S., et al. Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. 2001, 98(26):15149-15154.
30. Geusebroek J.M., Burghouts G.J., Smeulders A.W.M. The Amsterdam library of object images. Int. J. Comput. Vis. 2005, 61(1):103-112.
31. A.M. Martinez, R. Benavente, The AR face database Technical Report, CVC Technical Report #24, Purdue University, West Lafayette, 1998.
32. O.R. Laboratory, The Olivetti & Oracle Research Laboratory Face Database of Faces [Online]. Available: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
33. Cheng M., Fang B., Tang Y.Y., Chen H. A computational and theoretical analysis of local null space discriminant method for pattern classification, IJPRAI 2011, 25(1):117-134.