A flexible and efficient algorithm for regularized fisher discriminant analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5782 LNAI, Issue PART 2, 2009, Pages 632-647

  Zhang, Zhihua ^a   Dai, Guang ^a   Jordan, Michael I ^b  

^a ZHEJIANG UNIVERSITY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATA; DIMENSIONALITY REDUCTION; EFFICIENT ALGORITHM; EFFICIENT IMPLEMENTATION; FISHER DISCRIMINANT ANALYSIS; FISHER LINEAR DISCRIMINANT ANALYSIS; KERNEL DISCRIMINANT ANALYSIS; MEAN SQUARED ERROR; ORDINARY LEAST SQUARES; PRACTICAL PROBLEMS; PSEUDO-INVERSES; REGULARIZED DISCRIMINANT ANALYSIS; RIDGE REGRESSION;

FACE RECOGNITION; FISHER INFORMATION MATRIX; LEARNING ALGORITHMS; REGRESSION ANALYSIS; ROBOT LEARNING;

DISCRIMINANT ANALYSIS;

EID: 70349952327     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04174-7_41     Document Type: Conference Paper

References (21)

1. Baudat, G., Anouar, F.: Generalized discriminant analysis using a kernel approach. Neural Computation 12, 2385-2404 (2000)
2. Cheng, Y.-Q., Zhuang, Y.-M., Yang, J.-Y.: Optimal Fisher discriminant analysis using the rank decomposition. Pattern Recognition 25(1), 101-111 (1992)
3. Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley and Sons, New York (2001)
4. Friedman, J.H.: Regularized discriminant analysis. Journal of the American Statistical Association 84(405), 165-175 (1989)
5. Golub, G.H., Heath, M., Wahba, G.: Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21, 215-223 (1979)
6. Golub, G.H., Van Loan, C.F.: Matrix Computations. Johns Hopkins University Press, Baltimore (1996)
7. Hastie, T., Tishiran, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)
8. Hoerl, A.E., Kennard, R.W.: Ridge regression. Technometrics 12, 56-67, 69-82 (1970)
9. Horn, R.A., Johnson, C.R.:Matrix Analysis. Cambridge University Press, Cambridge (1985)
10. Howland, P., Jeon, M., Park, H.: Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications 25(1), 165-179 (2003)
11. Howland, P., Park, H.: Generalizing discriminant analysis using the generalized singular value decomposition. IEEE Transactions on Pattern Analysis andMachine Intelligence 26(8), 995-1006 (2004)
12. Kittler, J., Young, P.C.: A new approach to feature selection based on the Karhunen-Lòeve expansion. Pattern Recognition 5, 335-352 (1973)
13. Mika, S., Rätsch, G., Weston, J., Schölkopf, B., Smola, A., Müller, K.R.: Invariant feature extraction and classification in kernel space. In: Advances in Neural Information Processing Systems 12, vol.12, pp. 526-532 (2000)
14. Paige, C.C., Saunders, M.A.: Towards a generalized singular value decomposition. SIAM Journal on Numerical Analysis 18(3), 398-405 (1981)
15. Park, C.H., Park, H.: Nonlinear discriminant analysis using kernel functions and the generalized singular value decomposition. SIAM Journal on Matrix Analysis and Applications 27(1), 87-102 (2005)
16. Park, C.H., Park, H.: A relationship between linear discriminant analysis and the generalized minimum squared error solution. SIAM Journal on Matrix Analysis and Applications 27(2), 474-492 (2005)
17. Roth, V., Steinhage, V.: Nonlinear discriminant analysis using kernel functions. In: Advances in Neural Information Processing Systems 12, vol.12, pp. 568-574 (2000)
18. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
19. Webb, A.R.: Statistical Pattern Recognition. John Wiley & Sons, Hoboken (2002)
20. Ye, J.: Least squares linear discriminant analysis. In: The Twenty-Fourth International Coference on Machine Learning (ICML) (2007)
21. Ye, J., Li, Q., Xiong, H., Park, H., Janardan, R., Kumar, V.: An incremental dimension reduction algorithm via QR decomposition. In: ACM SIGKDD, pp. 364-373 (2004)