Disguised discrimination of locality-based unsupervised dimensionality reduction

International Journal of Pattern Recognition and Artificial Intelligence

Volumn 24, Issue 7, 2010, Pages 1011-1025

  Yang, Bo ^a,b   Chen, Songcan ^a  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b TONGHUA NORMAL UNIVERSITY   (China)

Author keywords

class information; dimensionality reduction; disguised discrimination; locality; Unsupervised learning

Indexed keywords

CLASS INFORMATION; CLASSIFICATION PERFORMANCE; CLASSIFICATION TASKS; DATA SETS; DIMENSIONALITY REDUCTION; DISGUISED DISCRIMINATION; LOCALITY; UNSUPERVISED ALGORITHMS;

UNSUPERVISED LEARNING;

ALGORITHMS;

EID: 78649385790     PISSN: 02180014     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0218001410008275     Document Type: Article

References (31)

1. O. Aran, O. T. Yildiz and E. Alpaydin, An incremental framework based on crossvalidation for estimating the architecture of a multilayer perceptron, Int. J. Patt. Recogn. Artif. Intell. 23(2009) 159-190.
2. M. A. Amin and H. Yan, An empirical study on the characteristics of gabor representations for face recognition, Int. J. Patt. Recogn. Artif. Intell. 23(2009) 401-431.
3. A. Argyriou, M. Herbster and M. Pontil, Combining graph laplacians for semi-supervised learning, NIPS (2005), pp. 67-74.
4. M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, NIPS (2001).
5. D. Cai, X. He and J. Han, Semi-supervised discriminant analysis, ICCV, Rio de Janeiro, Brazil (2007).
6. D. Cai, X. He, J. Han and H. Zhang, Orthogonal Laplacianfaces for face recognition, IEEE Trans. Imag. Process. 15(2006) 3608-3614.
7. H.-T. Chen, H.-W. Chang and T.-L. Liu, Local discriminant embedding and its variants, CVPR (2005), pp. 846-853.
8. S. Chen, B. Lovell and T. Shan, Robust adapted principal component analysis face recognition, Int. J. Patt. Recogn. Artif. Intell. 23(2009) 491-520.
9. W. Deng, J. Hu, J. Guo, H. Zhang and C. Zhang, Comments on globally maximizing, locally minimizing: Unsupervised discriminant projection with applications to face and palm biometrics, IEEE Trans. Patt. Anal. Mach. Intell. 30(2008) 1503-1504.
10. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd edn. (John Wiley & Sons, New York, 2001).
11. J. Gao, P. Kwan and X. Huang, Comprehensive analysis for the local Fisher discriminant analysis, Int. J. Patt. Recogn. Artif. Intell. 23(2009) 1129-1143.
12. X. He and P. Niyogi, Locality preserving projections, NIPS (2003).
13. X. He, S. Yan, Y. Hu, P. Niyogi and H.-J. Zhang, Face recognition using Laplacianfaces, IEEE Trans. Patt. Anal. Mach. Intell. 27(2005) 328-340.
14. T. Jebara, J. Wang and S. Chang, Graph construction and b-matching for semisupervised learning, ICML (2009), pp. 441-448.
15. I. T. Jolliffe, Principal Component Analysis (Springer Verlag, New York, 1986).
16. E. Krupka and N. Tishby, Incorporating prior knowledge on features into learning, AISTATS (2007), pp. 227-234.
17. W. Liu, K. Yuan, J. Wu, D. Ye, Z. Ji and S. Chen, Combining generalized NMF and discriminative mixture for classification of gene expression, Int. J. Patt. Recogn. Artif. Intell. 22(2008) 1587-1598.
18. A. M. Martinez and R. Benavente, The AR face database, CVC Technical Report #24, 1998.
19. Y. Moses, Y. Adini and S. Ullman, Face recognition: The problem of compensating for changes in illumination direction, ECCV (1994), pp. 286-296.
20. T. Ojala, M. Pietikäinen and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Patt. Anal. Mach. Intell. 24(2002) 971-987.
21. L. Qiao, S. Chen and X. Tan, Sparsity preserving projections with applications to face recognition, Patt. Recogn. 43(2010) 331-341.
22. K. Riesen and H. Bunke, Graph classification based on vector space embedding, Int. J. Patt. Recogn. Artif. Intell. 23(2009) 1053-1081.
23. F. Wang and C. Zhang, Feature extraction by maximizing the average neighborhood margin, CVPR (2007), pp. 17-22.
24. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang and S. Lin, Graph embedding and extension: A general framework for dimensionality reduction, IEEE Trans. Patt. Anal. Mach. Intell. 29(2007) 40-51.
25. S. Yan and H. Wang, Semi-supervised learning by sparse representation, SIAMInt. Conf. Data Mining, SDM (2009), pp. 792-801.
26. X. Yang, H. Fu, H. Zha and J. L. Barlow, Semi-supervised nonlinear dimensionality reduction, ICML (2006), pp. 1065-1072.
27. X. Yang, Q. Song and Y. Wang, A weighted support vector machine for data classification, Int. J. Patt. Recogn. Artif. Intell. 21(2007) 961-976.
28. A. Yang, J. Wright, Y. Ma and S. Sastry, Feature selection in face recognition: A sparse representation perspective, UC Berkeley Tech Report UCB/EECS-2007-99, 2007.
29. J. Yang, D. Zhang, J.-Y. Yang and B. Niu, Globally maximizing, locally minimizing: Unsupervised discriminant projection with applications to face and palm biometrics, IEEE Trans. Patt. Anal. Mach. Intell. 29(2007) 650-664.
30. Q. You, N. Zheng, L. Gao et al., Analysis of solution for supervised graph embedding, Int. J. Patt. Recogn. Artif. Intell. 22(2008) 1283-1299.
31. X. Zhu, Semi-supervised learning literature survey, Computer Sciences Technical Report 1530 (University of Wisconsin-Madison), 2008.