Large margin based nonnegative matrix factorization and partial least squares regression for face recognition

Pattern Recognition Letters

Volumn 32, Issue 14, 2011, Pages 1822-1835

  Pan, Ji Yuan ^a   Zhang, Jiang She ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Face recognition; Feature extraction; Large margin learning; Nonnegative matrix factorization; Out of sample

Indexed keywords

BASIS MATRIX; CONVERGENCY; K-NEAREST NEIGHBORS; LARGE MARGIN LEARNING; LOW-DIMENSIONAL REPRESENTATION; NONNEGATIVE MATRIX FACTORIZATION; OBJECTIVE FUNCTIONS; OUT-OF-SAMPLE; OUT-OF-SAMPLE EXTENSION; PARTIAL LEAST-SQUARES REGRESSION; SEPARATION STRUCTURES; TRAINING DATA;

ENCODING (SYMBOLS); FACE RECOGNITION; FACTORIZATION; FEATURE EXTRACTION; LEAST SQUARES APPROXIMATIONS; SEPARATION; VECTORS;

MATRIX ALGEBRA;

EID: 80052600921     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.07.015     Document Type: Article

References (30)

1. M.S. Bartlett, J.R. Movellan, and T.J. Sejnowski Face recognition by independent component analysis IEEE Trans. Neural Networks 13 2002 1450 1464 (Pubitemid 35428531)
2. P.M. Belhumeur, J.P. Hespanha, and D.J. Kriegman Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection IEEE Trans. Pattern Anal. Machine Intell. 1997 711 720 (Pubitemid 127762263)
3. G. Buchsbaum, and O. Bloch Color categories revealed by nonnegative matrix factorization of Munsell color spectra Vision Res. 42 2002 559 563 (Pubitemid 34164261)
4. Feng, T., Li, S.Z., Shum, H.Y., Zhang, H., 2002. Local nonnegative matrix factorization as a visual representation. In: Proc. Second Internat. Conf. on Development and Learning (ICDL '02).
5. Guillamet, D., Vitria, J., 2002. Nonnegative matrix factorization for face recognition. In: Proc. Conf. on Topics in Artificial Intelligence, pp. 336-344.
6. P. Geladi, and B. Kowalski Partial least squares regression: A tutorial Anal. Chim. Acta 1986 1 17
7. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang Face recognition using Laplacian faces IEEE Trans. Pattern Anal. Machine Intell. 27 3 2005 328 340 (Pubitemid 40377326)
8. P.O. Hoyer Nonnegative matrix factorization with sparseness constraints J. Machine Learn. Res. 5 2004 1457 1469
9. H. Hu ICA-based neighborhood preserving analysis for face recognition Comput. Vision Image Understand. 112 3 2008 286 295
10. A. Hyvarinen, and E. Oja Independent component analysis: Algorithms and applications Neural Network 13 2000 411 430 (Pubitemid 30447427)
11. I. Kotsia, S. Zafeirious, and I. Pitas A novel discriminant nonnegative matrix factorization algorithm with applications to facial image characterization problems IEEE Trans. Inform. Forensics Security 2 3 2007 588 595 (Pubitemid 47290591)
12. D.D. Lee, and H.S. Seung Learning the parts of objects by nonnegative matrix factorization Nature 401 1999 788 791
13. D.D. Lee, and H.S. Seung Algorithms for nonnegative matrix factorization Adv. Neural Inform. Process. Systems 13 2001 556 562
14. Liang, Z., et al., 2010. Projected gradient method for kernel discriminant nonnegative matrix factorization and the applications. Signal Process. doi:10.1016/j.sigpro.2010.01.019.
15. X. Liu, S. Yan, and H. Jin Projective nonnegative graph embedding IEEE Trans. Image Process. 19 2010
16. AT&T Laboratories Cambridge, ORL Face Database, 2005.
17. A. Pascual-Montano, J.M. Carazo, K. Kochi, D. Lehmean, and R. Pascual-Marqui Nonsmooth nonnegative matrix factorization (nsNMF) IEEE Trans. Pattern Anal. Machine Intell. 28 3 2006 403 415 (Pubitemid 43159636)
18. Ramanath, R., Snyder, W.E., Qi, H., 2003. Eigenviews for objective recognition in multispectral imaging systems. In: Proc. Applied Imagery Pattern Recognition (AIPR) Workshop.
19. Rosipal, R., Kramer, N., 2006. Overview and recent advances in partial least squares. In: Subspace, Latent Structure and Feature Selection Techniques, Springer, pp. 34-51. (Pubitemid 44029878)
20. Smarragdis, P., Brown, J.C., 2003. Nonnegative matrix factorization for polyphonic music transcription. In: Proc. IEEE workshop Applications of Signal Processing to Audio and Acoustics.
21. Wang, Y., Jia, Y., Hu, C., 2004. Fisher nonnegative matrix factorization for learning local features. In: Asian Conference on Computer Vision, Korea.
22. K.Q. Weinberger, and L. Saul Distance metric learning for large margin nearest neighbor classification J. Machine Learn. Res. 10 2009 207 244
23. H. Wold Soft modeling by latent variables: The nonlinear iterative partial least squares approach J. Gani, Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett 1975 Academic Press London 520 540
24. S. Wold, H. Ruhe, H. Wold, and W.J. Dunn III The collinearity problem in linear regression, the partial least squares (PLS) approach to generalized inverse SIAM J. Sci. Statist. Comput. 5 1984 735 743
25. H. Wold Path models with latent variables: The NIPALS approach H.M. Blalock, Quantitative Sociology: International Perspectives on Mathematical and Statistical Model Building 1975 Academic Press 307 357
26. Yang, J., Yan, S., Fu, Y., Li, X., Huang, T., 2008. Nonnegative graph embedding. In: IEEE Conf. on Computer Vision and Pattern Recognition.
27. S. Yan, D. Xu, B. Zhang, Q. Yang, H. Zhang, and S. Lin Graph embedding and extensions: A general framework for dimensionality reduction IEEE Trans. Pattern Anal. Machine Intell. 29 2007 40 51 (Pubitemid 46415944)
28. Yuan, Z., Oja, E., 2004. Projective nonnegative matrix factorization for image compression and feature extraction. In: Proc. Asian Conf. on Computer Vision, pp. 1-8.
29. S. Zafeiriou, A. Tefas, I. Buciu, and I. Pitas Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification IEEE Trans. Neural Network 17 3 2006 683 695
30. C.H. Zheng, D.S. Huang, L. Zhang, and X.Z. Kong Tumor clustering using nonnegative matrix factorization with gene selection IEEE Trans. Inform. Technol. Biomed. 2009 599 607