Discriminant analysis: A unified approach

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 514-521

  Zhang, Peng ^a   Peng, Jing ^a   Riedel, Norbert ^b  

^a TULANE UNIVERSITY   (United States)

^b Tulane University

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; ERROR CORRECTION; PROBLEM SOLVING; STATISTICAL METHODS; SUPPORT VECTOR MACHINES;

ERROR BOUND; LINEAR DISCRIMINANT ANALYSIS (LDA); STATISTICAL LEARNING THEORY;

DATA REDUCTION;

EID: 34548588868     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.51     Document Type: Conference Paper

References (24)

1. M. Bartlett. Further aspects of the theory of multiple regression. In Proceedings of the Cambridge Philosophical Society, number 34, 1938.
2. V. Belhumeur, J. Hespanha, and D. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(7):711-720, 1997.
3. L. Chen, H. M. Liao, M.T.Ko, J. Lin, and G. Yu. A new Ida-based face recognition system which can solve the small sample size problem. Pattern Recognition, 33:1713-1726, 2001.
4. F. Cucker and S. Smale. On the mathematical foundations of learning. Bulletin of the American Mathematical Society, 39(1):1-9, 2001.
5. F. Cucker and S. Smale. Best choices for regularization parameters in learning theory: On the bias-variance problem. Foundations Comput. Math., (4):413-428, 2002.
6. R. O. Duda and P. E. Hart. Pattern Classification and Scene Analysis. John Wiley-Sons, New York, l edition, 1973.
7. T. Evgeniou, M. Pontil, and T. Poggio. Regularization networks and support vector machines. Advances in Computational Mathematics, 13(1): 1-50, 2000.
8. R. Fisher. The use of multiple measurements in taxonomic problems. Ann. Eugenics, 7:178-188, 1936.
9. J. H. Friedman. Regularized discriminant analysis. Journal of the American Statistical Association, 84(405):165-175, 1989.
10. K. Fukunaga. Introduction to statistical pattern recognition. Academic Press, 1990.
11. G. Fung and O. L. Mangasarian. Proximal support vector machine classifiers. In F. Provost and R. Srikant, editors, Proceedings KDD-2001: Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, CA, pages 7786, New York, 2001.
12. A. Hoerl and R. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(3):55-67, 1970.
13. A. K. J. Hertz and R. Palmer. Introduction to the Theory of Neural Computation. Addison Wesley, 1991.
14. J.Yang, A.F.Frangi, J.-Y. Yang, D. Zhang, and Z. Jin. Kpca plus Ida: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans, on Pattern Analysis and Machine Intelligence, 27:230-244, 2005.
15. K. Liu, Y. Cheng, and J. Yang. A generalized optimal set of discriminant vectors. Pattern Recognition, 25(7):731-739, 1992.
16. J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos. Face recognition using kernel direct discriminant analysis algorithms. IEEE Transactions on Neural Networks, 14(1):117-126, 2003.
17. S. Mika. Kernel Fisher Discriminants. PhD thesis, University of Technology, Berlin, October 2002.
18. T. Poggio and S. Smale. The mathematics of learning: Dealing with data. Notices of the American Mathematical Society, 50(5):537-544, 2003.
19. J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle. Least Squares Support Vector Machines. World Scientific Pub. Co., Singapore, 2002.
20. D. Swets and J. Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(8):831-836, 1996.
21. A. N. Tikhonov and V. Y. Arsenin. Solutions of Ill-posed problems. John Wiley and Sons, Washington D.C., 1977.
22. V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.
23. H. Yu and J. Yang. A direct Ida algorithm for highdimension data with application to face recognition. Pattern Recognition, 34:2067-2070, 2001.
24. P. Zhang and J. Peng. Svm vs regularized least squares classification. In Proceedings of IEEE International Conference on Pattern Recognition, volume 1, pages 176-179, 2004.