Matching pursuit kernel fisher discriminant analysis

Journal of Machine Learning Research

Volumn 5, Issue , 2009, Pages 121-128

  Diethe, Tom ^a   Hussain, Zakria ^a   Hardoon, David R ^a   Shawe Taylor, John ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM MATCHING; BOUNDING TECHNIQUES; DATA SETS; ERROR BOUND; GENERALISATION; HIGH-DIMENSIONAL; KERNEL FISHER DISCRIMINANT ANALYSIS; MATCHING PURSUIT; MINIMAX ALGORITHM; REAL-WORLD DATASETS; SAMPLE COMPRESSION;

ERROR ANALYSIS;

ARTIFICIAL INTELLIGENCE;

EID: 81855170064     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (24)

1. Francis R. Bach and Michael I. Jordan. Predictive lowrank decomposition for kernel methods. In ICML'05: Proceedings of the 22nd international conference on Machine learning, pages 33-40, New York, NY, USA, 2005. ACM.
2. Peter L. Bartlett and Ambuj Tewari. Sparseness vs estimating conditional probabilities: Some asymptotic results. Journal of Machine Learning Research, 8: 775-790, 2007.
3. Scott Shaobing Chen, David L. Donoho, and Michael A. Saunders. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1):33-61, 1998.
4. Murat Dundar, Glenn Fung, Jinbo Bi, Sandilya Sathyakama, and Bharat Rao. Sparse fisher discriminant analysis for computer aided detection. In Proceedings of the SIAM International Conference on Data Mining, 2005.
5. Yuanjian Feng and Pengfei Shi. Face detection based on kernel fisher discriminant analysis. In Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004.
6. Roland. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7:179-188, 1936.
7. Sally Floyd and Manfred Warmuth. Sample compression, learnability, and the Vapnik-Chervonenkis dimension. Machine Learning, 21(3):269-304, 1995.
8. Isabelle Guyon, Asa Ben Hur, Steve Gunn, and Gideon Dror. Result analysis of the NIPS 2003 feature selection challenge. In Advances in Neural Information Processing Systems 17, pages 545-552. MIT Press, 2004.
9. Zakria Hussain and John Shawe-Taylor. Theory of matching pursuit. Neural Information Processing Systems, 2008.
10. Seung-Jean Kim, Alessandro Magnani, and Stephen P. Boyd. Robust fisher discriminant analysis. In In Advances in Neural Information Processing Systems, pages 659-666. MIT Press, 2006.
11. Brian Kulis, Ḿatýas Sustik, and Inderjit Dhillon. Learning low-rank kernel matrices. In ICML'06: Proceedings of the 23rd international conference on Machine learning, pages 505-512, New York, NY, USA, 2006. ACM.
12. Gert R.G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, and Michael I. Jordan. A robust minimax approach to classification. J. Mach. Learn. Res., 3:555-582, 2003. ISSN 1533-7928.
13. Nick Littlestone and Manfred Warmuth. Relating data compression and learnability. Technical report, University of California Santa Cruz, 1986.
14. Stéphane Mallat and Zhifeng Zhang. Matching pursuit with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12):3397-3415, 1993.
15. S. Mika, A. J. Smola, and B. Schölkopf. An improved training algorithm for kernel fisher discriminants. In In AISTATS, pages 98-104, 2001.
16. Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, and Klaus-Robert Müller. Fisher discriminant analysis with kernels. In E. Wilson Y. H. Hu, J. Larsen and S. Douglas, editors, Proc. NNSP'99, pages 41-48. IEEE, 1999.
17. Yagyensh Chandra Pati, Ramin Rezaiifar, and P.S. Krishnaprasad. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In Proceedings of the 27th Annual Asilomar Conference on Signals, Systems, and Computers, pages 40-45, 1993.
18. John Shawe-Taylor and Nello Cristianini. Estimating the moments of a random vector. In Proceedings of GRETSI 2003 Conference, volume 1, page 4752, 2003.
19. John Shawe-Taylor and Nello Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, U.K., 2004.
20. Alex J. Smola and Bernhard Schölkopf. Sparse greedy matrix approximation for machine learning. In Proceedings of 17th International Conference on Machine Learning, pages 911-918. Morgan Kaufmann, San Francisco, CA, 2000.
21. Lelsie G. Valiant. A theory of the learnable. Communications of the Association of Computing Machinery, 27(11):1134-1142, November 1984.
22. Pascal Vincent and Yoshua Bengio. Kernel matching pursuit. Machine Learning, 48(1-3):165-187, 2002.
23. Christopher K. I.Williams and Matthias Seeger. Using the nystr̈om method to speed up kernel machines. In Advances in Neural Information Processing Systems, volume 13, pages 682-688. MIT Press, 2001.
24. Hong-Jie Xing, Yu-Jiu Yang, Yong Wang, and Bao-Gang Hu. Sparse kernel fisher discriminant analysis. Springer Lecture Notes in Computer Science, 3496: 824-830, 2005.