Semisupervised generalized discriminant analysis

IEEE Transactions on Neural Networks

Volumn 22, Issue 8, 2011, Pages 1207-1217

  Zhang, Yu ^a   Yeung, Dit Yan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Constrained concave convex procedure; dimensionality reduction; generalized discriminant analysis; semisupervised learning

Indexed keywords

CONSTRAINED OPTIMIZATION;

CONSTRAINED CONCAVE-CONVEX PROCEDURES; DIMENSIONALITY REDUCTION; GENERALIZED DISCRIMINANT ANALYSIS; NONLINEAR PROJECTIONS; OPTIMALITY CRITERIA; OPTIMIZATION PROBLEMS; OPTIMIZATION PROCEDURES; SEMI- SUPERVISED LEARNING;

DISCRIMINANT ANALYSIS;

EID: 80051548569     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2011.2156808     Document Type: Article

References (46)

1. R. A. Fisher, "The use of multiple measurements in taxonomic problems," Ann. Eugen., vol. 7, no. 2, pp. 179-188, 1936.
2. C. R. Rao, "The utilization of multiple measurements in problems of biological classification," J. Royal Stat. Soc., vol. 10, no. 2, pp. 159-203, 1948.
3. L. Chen, H. Liao, M. Ko, J. Lin, and G. Yu, "A new LDA-based face recognition system which can solve the small sample size problem," Pattern Recog., vol. 33, no. 10, pp. 1713-1726, Oct. 2000.
4. W. J. Krzanowski, P. Jonathan, W. V. McCarthy, and M. R. Thomas, "Discriminant analysis with singular covariance matrices: Methods and applications to spectroscopic data," Appl. Stat., vol. 44, no. 1, pp. 101-115, 1995.
5. Y. Guo, T. Hastie, and R. Tibshirani, "Regularized linear discriminant analysis and its application in microarrays," Biostatistics, vol. 8, no. 1, pp. 86-100, 2007. (Pubitemid 44906104)
6. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces versus fisherfaces: Recognition using class specific linear projection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711-720, Jul. 1997. (Pubitemid 127762263)
7. J.-P. Ye and Q. Li, "A two-stage linear discriminant analysis via QRdecomposition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 6, pp. 929-941, Jun. 2005. (Pubitemid 40889843)
8. X. Wang and X. Tang, "Dual-space linear discriminant analysis for face recognition," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2. Washington D.C., Jun.-Jul. 2004, pp. 564-569.
9. I. T. Jolliffe, Principal Component Analysis. New York: Springer-Verlag, 1986.
10. H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, "Discriminative common vectors for face recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 1, pp. 4-13, Jan. 2005. (Pubitemid 40195893)
11. S. Ji and J. Ye, "Generalized linear discriminant analysis: A unified framework and efficient model selection," IEEE Trans. Neural Netw., vol. 19, no. 10, pp. 1768-1782, Oct. 2008.
12. J.-P. Ye, R. Janardan, and Q. Li, "2-D linear discriminant analysis," in Proc. Adv. Neural Inf. Process. Syst. 17, Vancouver, BC, Canada, 2004, pp. 1529-1536.
13. G. Baudat and F. Anouar, "Generalized discriminant analysis using a kernel approach," Neural Comput., vol. 12, no. 10, pp. 2385-2404, Oct. 2000.
14. J. Yang, A. F. Frangi, J.-Y. Yang, D. Zhang, and Z. Jin, "KPCA plus LDA: A complete kernel Fisher discriminant framework for feature extraction and recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 230-244, Feb. 2005. (Pubitemid 40377316)
15. T. Xiong, J. Ye, Q. Li, R. Janardan, and V. Cherkassky, "Efficient kernel discriminant analysis via QR decomposition," in Proc. Adv. Neural Inf. Process. Syst. 17, Vancouver, BC, Canada, 2004, pp. 1-8.
16. H. Cevikalp, M. Neamtu, and M. Wilkes, "Discriminative common vector method with kernels," IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1550-1565, Nov. 2006. (Pubitemid 44824267)
17. S. Ji and J. Ye, "Kernel uncorrelated and regularized discriminant analysis: A theoretical and computational study," IEEE Trans. Knowl. Data Eng., vol. 20, no. 10, pp. 1131-1321, Oct. 2008.
18. H. Kong, E. K. Teoh, J. G. Wang, and C. Kambhamettu, "Generalized 2-D Fisher discriminant analysis," in Proc. Brit. Mach. Vis. Conf., 2005, pp. 1-10.
19. G. R. G. Lanckriet, N. Cristianini, L. E. Ghaoui, P. L. Bartlett, and M. I. Jordan, "Learning the kernel matrix with semidefinite programming," J. Mach. Learn. Res., vol. 5, pp. 27-72, Jan. 2004.
20. S.-J. Kim, A. Magnani, and S. Boyd, "Optimal kernel selection in kernel fisher discriminant analysis," in Proc. 23rd Int. Conf. Mach. Learn., Pittsburgh, PA, 2006, pp. 465-472.
21. J.-P. Ye, J. Chen, and S. Ji, "Discriminant kernel and regularization parameter learning via semidefinite programming," in Proc. 24th Int. Conf. Mach. Learn., Corvalis, OR, 2007, pp. 1095-1102.
22. J.-P. Ye and T. Xiong, "Computational and theoretical analysis of null space and orthogonal linear discriminant analysis," J.Mach. Learn. Res., vol. 7, pp. 1183-1204, Jul. 2006. (Pubitemid 44015300)
23. J.-P. Ye, "Least squares linear discriminant analysis," in Proc. 24th Int. Conf. Mach. Learn., Corvalis, OR, 2007, pp. 1087-1093.
24. Z. Zhang, G. Dai, and M. I. Jordan, "A flexible and efficient algorithm for regularized Fisher discriminant analysis," in Proc. 20th Eur. Conf. Mach. Learn. Prin. Prac. Knowl. Discov. Databases, Bled, Slovenia, 2009, pp. 632-647.
25. O. Chapelle, A. Zien, and B. Schölkopf, Semi-Supervised Learning. Boston, MA: MIT Press, 2006.
26. X. Zhu, "Semi-supervised learning literature survey," Dept. Comput. Sci., Univ. Wisconsin-Madison, Madison, Tech. Rep. 1530, 2006.
27. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. Workshop Comput. Learn. Theory, Madison, WI, 1998, pp. 92-100.
28. K. Bennett and A. Demiriz, "Semi-supervised support vector machines," in Proc. Adv. Neural Inf. Process. Syst., Vancouver, BC, Canada, 1998, pp. 368-374.
29. T. Joachims, "Transductive inference for text classification using support vector machines," in Proc. 16th Int. Conf. Mach. Learn., San Francisco, CA, 1999, pp. 200-209.
30. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from examples," J. Mach. Learn. Res., vol. 7, pp. 2399-2434, Nov. 2006. (Pubitemid 44708005)
31. D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Schölkopf, "Learning with local and global consistency," in Proc. Adv. Neural Inf. Process. Syst. 16, Vancouver, BC, Canada, 2003, pp. 1-8.
32. X. Zhu, Z. Ghahramani, and J. Lafferty, "Semi-supervised learning using Gaussian fields and harmonic functions," in Proc. 20th Int. Conf. Mach. Learn., vol. 2. Washington D.C., 2003, pp. 912-919.
33. D. Cai, X. He, and J. Han, "Semi-supervised discriminant analysis," in Proc. IEEE Int. Conf. Comput. Vis., Rio de Janeiro, Brazil, Oct. 2007, pp. 1-7.
34. Y. Zhang and D.-Y. Yeung, "Semi-supervised discriminant analysis using robust path-based similarity," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Anchorage, AK, Jun. 2008, pp. 1-8.
35. Y. Zhang and D.-Y. Yeung, "Semi-supervised discriminant analysis via CCCP," in Proc. 19th Eur. Conf. Mach. Learn. Prin. Prac. Knowl. Discov. Databases, Antwerp, Belgium, 2008, pp. 644-659.
36. Y. Zhang and D.-Y. Yeung, "Heteroscedastic probabilistic linear discriminant analysis with semi-supervised extension," in Proc. 20th Eur. Conf. Mach. Learn. Prin. Prac. Knowl. Disc. Databases, Bled, Slovenia, 2009, pp. 602-616.
37. A. L. Yuille and A. Rangarajan, "The concave-convex procedure," Neural Comput., vol. 15, no. 4, pp. 915-936, Apr. 2003. (Pubitemid 37049812)
38. A. J. Smola, S. V. N. Vishwanathan, and T. Hofmann, "Kernel methods for missing variables," in Proc. 10th Int. Workshop Artif. Intell. Stat., Barbados, Jan. 2005, pp. 1-8.
39. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A. Smola, and K.-R. Müller, "Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 623-628, May 2003.
40. J. R. Schott, Matrix Analysis for Statistics, 2nd ed. New York: Wiley, 2005.
41. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
42. L. Zelnik-Manor and P. Perona, "Self-tuning spectral clustering," in Proc. Adv. Neural Inf. Process. Syst. 17, Vancouver, BC, Canada, 2004, pp. 1601-1608.
43. A. Asuncion and D. J. Newman. (2007). UCI Machine Learning Repository [Online]. Available: http://archive.ics.uci.edu/ml/
44. 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.
45. A. M. Martínez and R. Benavente, "The AR-face database," Centre Visió Comput., Univ. Autònoma Barcelona, Barcelona, Spain, Tech. Rep. 24, 1998.
46. J. Chen, J. Ye, and Q. Li, "Integrating global and local structures: A least squares framework for dimensionality reduction," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Minneapolis, MN, Jun. 2007, pp. 1-8.