Non-negative patch alignment framework

IEEE Transactions on Neural Networks

Volumn 22, Issue 8, 2011, Pages 1218-1230

  Guan, Naiyang ^a   Tao, Dacheng ^b   Luo, Zhigang ^a   Yuan, Bo ^c  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Image occlusion; non negative matrix factorization; patch alignment framework

Indexed keywords

CLASSIFICATION TASKS; COMMON PROPERTY; DATA SETS; DIMENSION REDUCTION ALGORITHM; FACE IMAGES; GRADIENT DESCENT; IMAGE OCCLUSION; MULTIPLICATIVE UPDATES; NON-NEGATIVE MATRIX FACTORIZATION; OPTIMAL STEP SIZE; PATCH ALIGNMENT FRAMEWORK; REAL-WORLD DATASETS;

ALIGNMENT; BLIND SOURCE SEPARATION; CLASSIFICATION (OF INFORMATION); EXPERIMENTS; FACTORIZATION; NEWTON-RAPHSON METHOD; OPTIMIZATION;

MATRIX ALGEBRA;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; CLASSIFICATION; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; FACTUAL DATABASE; METHODOLOGY;

ALGORITHMS; DATABASES, FACTUAL; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED;

EID: 80051544749     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2157359     Document Type: Article

References (33)

1. H. Hotelling, "Analysis of a complex of statistical variables into principal components," J. Educ. Psychol., vol. 24, no. 6, pp. 417-441, Sep. 1933.
2. R. A. Fisher, "The use of multiple measurements in taxonomic problems," Ann. Eugen., vol. 7, no. 7, pp. 179-188, 1936.
3. D. D. Lee and H. S. Seung, "Learning the parts of objects by nonnegative matrix factorization," Nature, vol. 401, no. 21, pp. 788-791, Oct. 1999.
4. 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 Netw., vol. 17, no. 3, pp. 683-695, May 2006.
5. D. Cai, X. He, J. Han, and T. Huang, "Graph regularized nonnegative matrix factorization for data representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. PP, no. 99, p. 1, Dec. 2010.
6. S. Z. Li, X. W. Hou, H. J. Zhang, and Q. S. Cheng, "Learning spatially localized, parts-based representation," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., vol. 2. Dec. 2001, pp. 207-212.
7. E. Alpaydin, "Combined 5 × 2 cv F test for comparing supervised classification learning algorithms," Neural Comput., vol. 11, no. 8, pp. 1885-1892, 1999.
8. 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.
9. W. Bian and D. Tao, "Max-min distance analysis by using sequential SDP relaxation for dimension reduction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 1037-1050, May 2011.
10. 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.
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. (Pubitemid 32041578)
12. J. Tenenbaum, V. Silva, and J. Langford, "A global geometric framework for nonlinear dimensionality reduction," Science, vol. 290, no. 5500, pp. 2319-2323, Dec. 2000. (Pubitemid 32041577)
13. X. He and P. Niyogi, "Locality preserving projections," in Proc. Adv. Neural Inf. Process. Syst., vol. 15. 2003, pp. 1-8.
14. T. Zhang, D. Tao, and J. Yang, "Discriminative locality alignment," in Proc. 10th Eur. Conf. Comput. Vis., vol. 5302. 2008, pp. 725-738.
15. Z. Zhang and H. Zha, "Principal manifolds and nonlinear dimension reduction via local tangent space alignment," SIAM J. Sci. Comput., vol. 26, no. 1, pp. 313-338, 2005. (Pubitemid 40297703)
16. D. P. Bertsekas, Nonlinear Programming, 2nd ed. Belmont, MA: Athena Scientific, 1999.
17. N. Guan, D. Tao, Z. Luo, and B. Yuan, "Fast gradient descent for nonnegative patch alignment framework," Univ. Technol., Sydney (UTS), Sydney, Australia, Tech. Rep.-2010-11, 2010.
18. C. H. Q. Ding, T. Li, and M. Jordan, "Convex and semi-nonnegative matrix factorizations," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 1, pp. 45-55, Jan. 2010.
19. R. Zdunek and A. Cichocki, "Non-negative matrix factorization with quasi-Newton optimization," in Proc. 8th Int. Conf. Artif. Intell. Soft Comput., vol. 4029. 2006, pp. 870-879. (Pubitemid 44089876)
20. N. Guan, D. Tao, Z. Luo, and B. Yuan, "Manifold regularized discriminative non-negative matrix factorization with fast gradient descent," IEEE Trans. Image Process., vol. 20, no. 7, pp. 2030-2048, Jul. 2011.
21. R. A. Horn and C. R. Johnson, Matrix Analysis. Cambridge, U.K.: Cambridge Univ. Press, 1985.
22. M. A. Woodbury, "Inverting modified matrices," Stat. Res. Group, Princeton Univ., Princeton, NJ, Memo. Rep. 42, 1950.
23. C.-J. Lin, "On the convergence of multiplicative update algorithms for nonnegative matrix factorization," IEEE Trans. Neural Netw., vol. 18, no. 6, pp. 1589-1596, Nov. 2007. (Pubitemid 350148414)
24. J. Yang, S. Yan, Y. Fu, X. Li, and T. Huang, "Non-negative graph embedding," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 4. Anchorage, AK, Jun. 2008, pp. 1-8.
25. C. Wang, Z. Song, S. Yan, L. Zhang, and H. Zhang, "Multiplicative nonnegative graph embedding," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 5. Miami, FL, Jun. 2009, pp. 389-396.
26. D. Cai, X. He, and J. Han, "SRDA: An efficient algorithm for largescale discriminant analysis," IEEE Trans. Knowl. Data Eng., vol. 20, no. 1, pp. 1-12, Jan. 2008.
27. C.-J. Lin, "Projected gradient methods for nonnegative matrix factorization," Neural Comput., vol. 19, no. 10, pp. 2756-2779, Oct. 2007.
28. M. Belkin, "Problems of learning on manifolds," Ph.D. thesis, Dept. Math., Univ. Chicago, Chicago, IL, Aug. 2003.
29. F. S. Samaria and A. C. Harter, "Parameterisation of a stochastic model for human face identification," in Proc. 2nd IEEE Workshop Appl. Comput. Vis., Sarasota, FL, Dec. 1994, pp. 138-142.
30. D. B. Graham and N. M. Allinson, "Characterizing virtual eigensignatures for general purpose face recognition," in Proc. Face Recognit.: Theory Appl., vol. 163. 1998, pp. 446-456.
31. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
32. T. Dietterich, "Approximate statistical tests for comparing supervised classification learning algorithms," Neural Comput., vol. 10, no. 7, pp. 1895-1923, Oct. 1998. (Pubitemid 128463689)
33. T. Zhou, D. Tao, and X. Wu, "Manifold elastic net: A unified framework for sparse dimension reduction," Data Min. Knowl. Disc., vol. 22, no. 3, pp. 340-371, May 2011.