Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

Pattern Recognition

Volumn 45, Issue 4, 2012, Pages 1482-1499

  Zhao, Mingbo ^a   Zhang, Zhao ^a   Chow, Tommy W S ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Dimensionality reduction; Discriminative learning; Semi supervised learning; Trace ratio criterion

Indexed keywords

DATA POINTS; DATA SETS; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION METHOD; DISCRIMINATIVE LEARNING; EFFICIENT ALGORITHM; EUCLIDEAN DISTANCE; EXTENSIVE SIMULATIONS; GEOMETRICAL STRUCTURE; HIGH DIMENSIONAL DATA; MACHINE LEARNING APPLICATIONS; NON-LINEAR METHODS; NONLINEAR PROBLEMS; OPTIMAL SOLUTIONS; RATIO PROBLEM; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; TRACE RATIO CRITERION; TRAINING PROCEDURES; UNLABELED DATA;

ALGORITHMS; PATTERN RECOGNITION; SUPERVISED LEARNING; TRACE ANALYSIS;

DATA REDUCTION;

EID: 83655161380     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2011.10.008     Document Type: Article

References (46)

1. J.B. Tenenbaum, V. de Silva, and J.C. Langford A global geometric framework for nonlinear dimensionality reduction Science 290 5500 2000 2319 2323 (Pubitemid 32041577)
2. S. Roweis, and L. Saul Nonlinear dimensionality reduction by locally linear embedding Science 290 5500 2000 2323 2326 (Pubitemid 32041578)
3. Y. Guo, S. Li, J. Yang, T. Shu, and L. Wu A generalized FoleySammon transform based on generalized fisher discriminant criterion and its application to face recognition Pattern Recognition Letter 24 13 2003 147 158 (Pubitemid 36080765)
4. H. Wang, S. Yan, d. Xu, X. Tang, T. Huang, Trace ratio vs. ratio trace for dimensionality reduction, in: Proceedings of CVPR, 2007.
5. Y. Jia, F. Nie, and C. Zhang Trace ratio problem revisited IEEE Transactions on Neural Network 20 4 2009 729 735
6. S. Xiang, F. Nie, and C. Zhang Learning a Mahalanobis distance metric for data clustering and classification Pattern Recognition 41 12 2008 3600 3612
7. S. Yan, X. Tang, Trace quotient problem revisited, in: Proceedings of ECCV, 2006, pp. 232244.
8. C. Shen, H. Li, and M.J. Brooks Supervised dimensionality reduction via sequential semi-definite programming Pattern Recognition 41 12 2008 3644 3652
9. J. Ye, Least square linear discriminant analysis, in: Proceedings of ICML, 2007
10. R.A. Fisher The use of multiple measurements in taxonomic problems Annals of Eugenics 7 1936 179 188
11. P.N. Belhumeur., J.P. Hespanha, and D.J. Kriegman Eigenfaces vs. fisherfaces: recognition using class specific linear projection IEEE Transactions on Pattern analysis and Machine Intelligence 19 7 1997 711 720 (Pubitemid 127762263)
12. X. He, S. Yan, Y. Hu, P. Niyogi, and H. Zhang Face recognition using Laplacianfaces IEEE Transactions on Pattern Analysis and Machine Intelligence 27 3 2005 328 340 (Pubitemid 40377326)
13. M. Belkin, and P. Niyoqi Laplacian eigenmaps for dimensionality reduction and data representation Neural Computation 15 2003 1373 1396 (Pubitemid 37049796)
14. H. Li, T. Jiang, and K. Zhang Efficient and robust feature extraction by maximum margin criterion IEEE Transactions on Neural Networks 17 1 2006 157 165 (Pubitemid 43263948)
15. H. Hotelling Relations between two sets of variates Biometrika 28 34 1936 321 377
16. D.R. Hardoon, S. Szedmak, and J.S. Taylor Canonical correlation analysis: An overview with application to learning method Neural Computation 16 12 2004 2639 2664 (Pubitemid 39604012)
17. T. Sun, and S. Chen Class label versus sample label-based CCA Applied Mathematics and Computation 185 1 2007 272 283 (Pubitemid 46241364)
18. D. Cai, X. He, J. Han, Semi-supervised discriminant analysis, in: Proceedings of ICCV, 2007
19. D. Zhang, Z. Zhou, S. Chen, Semi-supervised dimensionality reduction, in: Proceedings of SDM, 2007
20. J. Chen, J. Ye, Q. Li, Integrating global and local structures: a least squares framework for dimensionality reduction, in: Proceedings of CVPR, 2007
21. Y. Song, F. Nie, C. Zhang, and S. Xiang A unified framework for semi-supervised dimensionality reduction Pattern Recognition 41 9 2008 2789 2799
22. M. B. Blaschko, C. H. Lampert, A. Gretton, Semi-supervised Laplacian regularization of kernel canonical correlation analysis, in: Proceedings of the ECML PKDD, 2008
23. F. Nie, S. Xiang, Y. Jia, and C. Zhang Semi-supervised orthogonal discriminant analysis via label propagation Pattern Recognition 42 11 2009 2515 2627
24. L. Chen, H. Liao, and M. Ko J. Lin and g. Yu, A new LDA-based face recognition system which can solve the small sample size problem Pattern Recognition 33 10 2000 1713 1726
25. Z. Jin, J.Y. Yang, Z.S. Hu, and Z. Lou Face recognition based on uncorrelated discriminant transformation Pattern Recognition 34 7 2001 1405 1416 (Pubitemid 32424906)
26. P. Howland, J. Wang, and H. Park Solving the small sample size problem in face recognition using generalized discriminant analysis Pattern Recognition 39 2 2006 277 287 (Pubitemid 41586095)
27. H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana Discrinative common vectors for face recognition IEEE Transactions on Pattern Analysis and Machine Intelligence 27 1 2005 4 13 (Pubitemid 40195893)
28. H. Hu Orthogonal neighborhood preserving discriminant analysis for face recognition Pattern Recognition 41 6 2008 2045 2054
29. E. Kokiopoulou, and Y. Saad Orthogonal neighborhood preserving projections: a projection-based dimensionality reduction technique IEEE Trans. on Pattern Analysis and Machine Intelligence 29 12 2007 2143 2156 (Pubitemid 350124063)
30. D. Cai, X. He, J. Han, and H. Zhang Orthogonal Laplacian faces for face recognition IEEE Transactions on Image Processing 15 11 2006 3608 3614 (Pubitemid 44615155)
31. J. Yang, A.F. Frangi, D. Zhang, J.Y. Yang, and J. Zhong KPCA plus LDA: a complete kernel fisher discriminant Framework for feature extraction and recognition IEEE Transactions on Pattern Analysis and Machine Intelligence 27 2 2005 230 244 (Pubitemid 40377316)
32. K.R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf An introduction to Kernel-based learning algorithms IEEE Transactions on Neural Networks 12 2 2001 181 201 (Pubitemid 32371478)
33. K. Fukuaga Introduction to Statistical Pattern classification 1990 Academic Press USA
34. R.A. Horn, and C.R. Johnson Matrix analysis 1990 Cambridge University Press Cambridge
35. F.R.K. Chung Spectral Graph Theory 1997 American Mathematical Society
36. D.B. Graham, and N.M. Allinson Characterizing virtual eigensignatures for general purpose face recognition in face recognition: from theory to application NATO ASI Series F, computer and Systems Sciences 163 1998 446 456
37. F.S. Samaria, and A.C. Harter Parameterization of a stochastic model for human face identification IEEE Workshop on Applications of Computer Vision 1994 138 142
38. J. Hull A database for handwritten text recognition research IEEE Transactions on Pattern Recognition and Machine Intelligence 16 5 1994 550 554
39. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner Gradient-based learning applied to document recognition Proceedings of IEEE 86 11 1998 2278 2324
40. S. A. Nene, S. K. Nayar, H. Murase, Columbia Object Image Library (COIL-100), Technical Report CUCS-006-96, 1996
41. A.M. Martinez, and A.C. Kak PCA versus LDA IEEE Transactions on Pattern Recognition and Machine Intelligence 23 2 2001 228 233 (Pubitemid 32254716)
42. J.Z. Wang, J. Li, and G. Wiederhold Simplicity: semantics sensitive integrated matching for picture libraries IEEE Transactions on Pattern Recognition and Machine Intelligence 23 9 2001 947 963 (Pubitemid 32981615)
43. J. Yang, and J.Y. Yang Why can LDA be performed in PCA transformed space? Pattern Recognition 36 2 2003 563 566 (Pubitemid 35212848)
44. K. Liu, Y.Q. Cheng, J.Y. Yang, and X. Liu An efficient algorithm for FoleySammon optimal set of discriminant vectors by algebraic method International Journal of Pattern Recognition and Artificial Intelligence 6 5 1992 817 829
45. Y. Pang, and Y. Yuan Outlier-resisting graph embedding Neurocomputing 73 46 2010 968 974
46. X. Wang, X. Gao, Y. Yuan, D. Tao, and J. Li Semi-supervised Gaussian process latent variable model with pairwise constraints Neurocomputing 73 1012 2010 2186 2195