Incremental embedding and learning in the local discriminant subspace with application to face recognition

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 40, Issue 5, 2010, Pages 580-591

  Cheng, Miao ^a   Fang, Bin ^a   Tang, Yuan Yan ^a   Zhang, Taiping ^a   Wen, Jing ^a  

^a CHONGQING UNIVERSITY   (China)

Author keywords

Dimensionality reduction; discriminant embedding; face recognition; incremental learning; manifold learning; singular value decomposition (SVD); small sample size (SSS) problem

Indexed keywords

DIMENSIONALITY REDUCTION; DISCRIMINANT EMBEDDING; INCREMENTAL LEARNING; MANIFOLD LEARNING; SINGULAR VALUE DECOMPOSITION (SVD); SMALL SAMPLE SIZE PROBLEMS;

DATA MINING; DATA REDUCTION; DISCRIMINANT ANALYSIS; FEATURE EXTRACTION; LEARNING ALGORITHMS; SINGULAR VALUE DECOMPOSITION;

FACE RECOGNITION;

EID: 77955850136     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2010.2043529     Document Type: Article

References (56)

1. M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cogn. Neu-rosci., vol.3, no.1, pp. 71-86, 1991.
2. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2000.
3. P. Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Mach. Intell., vol.19, no.7, pp. 711-720, Jul. 1997.
4. H. Yu and J. Yang, "A direct lda algorithm for high dimensional data-with application to face recognition," Pattern Recogn., vol.34, no.10, pp. 2067-2070, 2001.
5. J. Ye and Q. Li, "A two-stage linear discriminant analysis via qr-decomposition," IEEE Trans. Pattern Anal. Mach. Intell., vol.27, no.6, pp. 929-941, Jun. 2005.
6. D. Tao, X. Li, X. Wu, and S. J. Maybank, "Geometric mean for subspace selection," IEEE Trans. Pattern Anal. Mach. Intell., vol.31, no.2, pp. 260-274, Feb. 2009.
7. W. Bian and D. Tao, "Harmonic mean for subspace selection," in Proc. 19th Int. Conf. Pattern Recogn., 2008, pp. 1-4.
8. D. Tao, X. Li, X. Wu, and S. J. Maybank, "General averaged divergence analysis," in Proc. 7th IEEE Int. Conf. Data Mining, 2007, pp. 302-311.
9. R. A. Fisher, "The use of multiple measurements in taxonomic problems," Ann. Eugen., vol.7, pp. 179-188, 1936.
10. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol.290, no.5500, pp. 2319-2323, Dec. 2000.
11. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol.290, no.5500, pp. 2323-2326, Dec. 2000.
12. T. Kohonen, "Self-organizing maps," in Springer Series in Information Sciences, vol.30. Berlin, Germany: Springer-Verlag, 2001.
13. M. Belkin and P. Niyogi, "Laplacian eigenmaps and spectral techniques for embedding and clustering," Neural Inf. Process. Syst., vol.14, pp. 585-591, 2001.
14. X. He and P. Niyogi, "Locality preserving projections," Adv. Neural Inf. Process. Syst., vol.16, pp. 153-160, 2004.
15. X. He, S. Yan, Y. Hu, P. Niyogi, and H. J. Zhang, "Face recognition using laplacianfaces," IEEE Trans. Pattern Anal. Mach. Intell., vol.27, no.3, pp. 328-340, Mar. 2005.
16. H. T. Chen, H. W. Chang, and T. L. Liu, "Local discriminant embedding and its variants," in Proc. 10th IEEE Int. Conf. Comput. Vis. Pattern Recogn., 2005, vol.2, pp. 846-853.
17. W. Zhang, X. Xue, H. Lu, and Y. F. Guo, "Discriminant neighborhood embedding for classification," Pattern Recogn., vol.39, pp. 2240-2243, 2006.
18. X. Li, S. Lin, S. Yan, and D. Xu, "Discriminant locally linear embedding with high-order tensor data," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.38, no.2, pp. 342-352, Apr. 2008.
19. S. Yan, D. Xu, B. Y. Zhang, H. J. Zhang, Q. Yang, and S. Lin, "Graph embedding and extensions: A general framework for dimensionality reduction," IEEE Trans. Pattern Anal. Mach. Intell., vol.29, no.1, pp. 40-51, Jan. 2007.
20. J. Ham, D. D. Lee, S. Mika, and B. Schölkopf, "A kernel view of the dimensionality reduction of manifolds," in Proc. 21st Int. Conf. Mach. Learning, 2004, vol.69, pp. 369-376.
21. B. Schölkopf,A.Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol.10, no.5, pp. 1299-1319, Jul. 1998.
22. T. Zhang, D. Tao, X. Li, and J. Yang, "Discriminative locality alignment," in Proc. 10th Eur. Conf. Comput. Vis., 2008, pp. 725-738.
23. T. Zhang, D. Tao, X. Li, and J. Yang, "Patch alignment for dimensionality reduction," IEEE Trans. Knowl. Data Eng., vol.21, no.9, pp. 1299-1313, Sep. 2009.
24. T. Zhang, D. Tao, X. Li, and T. Yang, "A unifying framework for spectral analysis based dimensionality reduction," in Proc. IEEE Int. Joint Conf. Neural Netw., Jun. 2008, pp. 1671-1678.
25. R. F. Hauser, M. Friess, J. M. Küster, and J. Vanhatalo, "An incremental approach to the analysis and transformation of workflows using region trees," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol.38, no.3, pp. 347-359, May 2008.
26. J. Sun, D. Tao, S. Papadimitriou, P. S. Yu, and C. Faloutsos, "Incremental tensor analysis: Theory and applications," ACM Trans. Knowl. Discov. Data, vol.2, no.3, pp. 1-37, 2008.
27. J. Sun, D. Tao, and C. Faloutsos, "Beyond streams and graphs: Dynamic tensor analysis," in Proc. 12th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2006, pp. 374-383.
28. D. Tao, X. Tang, X. Li, and Y. Rui, "Kernel direct biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm," IEEE Trans. Multimedia, vol.8, no.4, pp. 716-727, Aug. 2006.
29. X. Li, S. J. Maybank, S. Yan, D. Tao, and D. Xu, "Gait components and their application to gender recognition," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol.38, no.2, pp. 145-155, Mar. 2008.
30. S. Bandyopadhyay, U. Maulik, and D. Roy, "Gene identification: Classical and computational intelligence approaches," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol.38, no.1, pp. 55-68, Jan. 2008.
31. W. Liu, D. Tao, and J. Liu, "Transductive component analysis," in Proc. IEEE Int. Conf. Data Mining, 2008, pp. 433-442.
32. R. Polikar, L. Udpa, S. S. Udpa, and V. Honavar, "Learn++: An incremental learning algorithm for supervised neural networks," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol.31, no.4, pp. 497-508, Nov. 2001.
33. H. Zhao and P. C. Yuen, "Incremental linear discriminant analysis for face recognition," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.38, no.1, pp. 201-221, Feb. 2008.
34. J. Weng,Y. Zhang, and W. S. Hwang, "Candid covariance-free incremental principal component analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol.25, no.8, pp. 1034-1040, Aug. 2003.
35. I. Dagher and R. Nachar, "Face recognition using ipca-ica algorithm," IEEE Trans. Pattern Anal. Mach. Intell., vol.28, no.6, pp. 996-1000, Jun. 2006.
36. T. D. Sanger, "Optimal unsupervised learning in a single-layer linear feedforward neural network," Neural Netw., vol.2, no.6, pp. 459-473, 1989.
37. D. Skočaj and A. Leonardis, "Incremental and robust learning of subspace representations," Image Vis. Comput., vol.26, no.1, pp. 27-38, Jan. 2008.
38. H. Zhao, P. C. Yuen, and J. T. Kwok, "A novel incremental principal component analysis and its application for face recognition," IEEE Trans. Syst., Man, Cybern. B. Cybern., vol.36, no.4, pp. 873-886, Aug. 2006.
39. Y. Li, "On incremental and robust subspace learning," Pattern Recogn., vol.37, no.7, pp. 1509-1518, Jul. 2004.
40. M. Artač, M. Jogan, and A. Leonardis, "Incremental pca or on-line visual learning and recognition," in Proc. Int. Conf. Pattern Recogn., Quebec, Canada, Aug. 2002, vol.3, pp. 781-784.
41. A. Levy and M. Lindenbaum, "Sequential karhunen-loeve basis extracion and its application to images," IEEE Trans. Image Process., vol.9, no.8, pp. 1371-1374, Oct. 2000.
42. P. Hall, D. Marshall, and R. Martin, "Merging and splitting eigenspace models," IEEE Trans. Pattern Anal. Mach. Intell., vol.22, no.9, pp. 1042-1049, Sep. 2000.
43. S. Pang, S. Ozawa, and N. Kasabov, "Incremental linear discriminant analysis for classification of data streams," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol.35, no.5, pp. 905-914, Oct. 2005.
44. J. Ye, Q. Li, H. Xiong, H. Park, R. Janardan, and V. Kumar, "Idr/qr: An incremental dimension reduction algorithm via qr decomposition," IEEE Trans. Knowl. Data Eng., vol.17, no.9, pp. 1208-1222, Sep. 2005.
45. G. H. Golub and C. F. V. Loan, Matrix Computations, 3rd ed. Baltimore, MD: Johns Hopkins Univ. Press, 1996.
46. P. Howland and H. Park, "Generalizing discriminant analysis using the generalized singular value decomposition," IEEE Trans. Pattern Anal. Mach. Intell., vol.26, no.8, pp. 995-1006, Aug. 2004.
47. H. Zha and H. D. Simon, "On updating problems in latent semantic indexing," SIAM J. Sci. Stat. Comput., vol.21, no.2, pp. 782-791, Sep./Oct. 1999.
48. M. Brand, "Incremental singular value decomposition of uncertain data with missing values," in Proc. 7th Eur. Conf. Comput. Vis. I, 2002, vol.2350, pp. 707-720.
49. J. R. Bunch and P. Nielsen, "Updating the singular value decomposition," Numer. Math., vol.31, pp. 111-129, 1978.
50. M. Gu and S. C. Eisenstat, "A stable and fast algorithm for updating the singular value decomposition," Dept. Comput. Sci., Yale Univ., New Haven, Tech. Rep. YALEU/DCS/RR-966, 1993.
51. T. Sim, S. Baker, and M. Bsat, "The cmu pose, illumination, and expression database," IEEE Trans. Pattern Anal. Mach. Intell., vol.25, no.12, pp. 1615-1618, Dec. 2003.
52. A. M. Martinez and R. Benavente, "The ar face database technical report," Purdue Univ., West Lafayette, CVC Tech. Rep. #24, Jun. 1998.
53. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, "From few to many: Illumination cone models for face recognition under variable lighting and pose," IEEE Trans. Pattern Anal. Mach. Intell., vol.23, no.6, pp. 643-660, Jun. 2001.
54. K. C. Lee, J. Ho, and D. Kriegman, "Acquiring linear subspaces for face recognition under variable lighting," IEEE Trans. Pattern Anal. Mach. Intell., vol.27, no.5, pp. 684-698, May 2005.
55. B. Fang, M. Cheng, Y. Y. Tang, and G. He, "Improving the discriminant ability of local margin based learning method by incorporating the global between-class separability criterion," Neurocomputing, vol.73, no.1-3, pp. 536-541, 2009.
56. J. Demsar, "Statistical comparisons of classifiers over multiple data sets," J. Mach. Learning Res., vol.7, pp. 1-30, 2006. (Pubitemid 43022939)