A semi-definite positive Linear Discriminant Analysis and its applications

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2012, Pages 942-947

  Kong, Deguang ^a   Ding, Chris ^a  

^a University of Texas at Arlington   (United States)

Author keywords

Kernel LDA; LDA; Multi class; Multilabel; Semi definite positive

Indexed keywords

KERNEL LDA; LDA; MULTI-CLASS; MULTI-LABEL; SEMI-DEFINITE POSITIVE;

DATA MINING; EIGENVALUES AND EIGENFUNCTIONS; REGRESSION ANALYSIS;

LAPLACE TRANSFORMS;

EID: 84874077173     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2012.111     Document Type: Conference Paper

References (28)

1. B. Alipanahi, M. Biggs, and A. Ghodsi. Distance metric learning vs. fisher discriminant analysis. In AAAI, 2008.
2. M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, pages 585-591, 2001.
3. C. M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.
4. S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
5. J. Chen, J. Ye, and Q. Li. Integrating global and local structures: A least squares framework for dimensionality reduction. In CVPR, 2007.
6. 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 Recognition, 33(10):1713-1726, October 2000.
7. F. Chung. Spectral Graph Theory. American Mathematical Society, 1997.
8. D. Dueck and B. J. Frey. Non-metric affinity propagation for unsupervised image categorization. In ICCV, 2007.
9. K. Fukunaga. Introduction to statistical pattern recognition. Academic Press, 1990.
10. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, 2001.
11. P. Howland, M. Jeon, and H. Park. Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 25:165-179, 2003.
12. D. Kong and C. H. Q. Ding. Maximum consistency preferential random walks. In ECML/PKDD (2), pages 339-354, 2012.
13. D. Kong, C. H. Q. Ding, and H. Huang. Robust nonnegative matrix factorization using l21-norm. In CIKM, pages 673-682, 2011.
14. D. Kong, C. H. Q. Ding, H. Huang, and F. Nie. An Iterative Locally Linear Embedding Algorithm. ArXiv e-prints, June 2012.
15. D. Kong, C. H. Q. Ding, H. Huang, and H. Zhao. Multi-label relieff and f-statistic feature selections for image annotation. In CVPR, pages 2352-2359, 2012.
16. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. In Proceedings of the IEEE, pages 2278-2324, 1998.
17. Y. J. Lee and K. Grauman. Foreground focus: Unsupervised learning from partially matching images. International Journal of Computer Vision, 85(2):143-166, 2009.
18. H. Li, T. Jiang, and K. Zhang. Efficient and robust feature extraction by maximum margin criterion. In Proceedings of Advances in Neural Information Processing Systems(NIPS 2003), 2003.
19. D. Luo, C. Ding, and H. Huang. Linear discriminant analysis: New formulations and overfit analysis. In AAAI, 2011.
20. H. Park, L. M. Jeon, and J. B. R. Z. Lower dimensional representation of text data based on centroids and least squares. BIT, 43:2003, 2003.
21. X. Tao, J. Ye, Q. Li, R. Janardan, and V. Cherkassky. Efficient kernel discriminant analysis via qr decomposition. In The Eighteenth Annual Conference on Neural Information Processing Systems (NIPS 2004), pages 1529-1536, 2004.
22. H. Wang, C. Ding, and H. Huang. Multi-label linear discriminant analysis. In The 11th European Conference on Computer Vision (ECCV), 2010.
23. J. Yan, B. Zhang, S. Yan, Q. Yang, and H. Li. Immc: incremental maximum margin criterion. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004.
24. J. Ye. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. The Journal of Machine Learning Research, 6, December 2005.
25. J. Ye. Characterization of a family of algorithms for generalized discriminant analysis on undersampled problems. Journal of Machine Learning, 6:483-502, Apr 2005.
26. J. Ye. Least squares linear discriminant analysis. In ICML, 2007.
27. J. Ye and S. Ji. Discriminant Analysis for Dimensionality Reduction: An Overview of Recent Developments, Biometrics: Theory, Methods & Applications. IEEE/Wiley, 2008.
28. X. Zhu. Semi-supervised learning. In Encyclopedia of Machine Learning, pages 892-897, 2010.