Randomized PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension

NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Volumn , Issue , 2006, Pages 1481-1488

  Warmuth, Manfred K ^a   Kuzmin, Dima ^a  

^a University of California Santa Cruz   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

'CURRENT; APPROXIMATION ERRORS; LOW-DIMENSIONAL SUBSPACE; ON-LINE ALGORITHMS; PCA ALGORITHMS; PRINCIPAL-COMPONENT ANALYSIS; QUADRATIC APPROXIMATION; REGRET BOUNDS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 34547980767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. R. Bhatia. Matrix Analysis. Springer, Berlin, 1997.
2. Olivier Bousquet and Manfred K. Warmuth. Tracking a small set of experts by mixing past posteriors. Journal of Machine Learning Research, 3:363-396, 2002.
3. N. Cesa-Bianchi, Y. Freund, D. Haussler, D. P. Helmbold, R. E. Schapire, and M. K. Warmuth. How to use expert advice. 44(3):427-485, 1997.
4. Koby Crammer. Online tracking of linear subspaces. In Proceedings of the 19th Annual Conference on Learning Theory (COLT 06), Pittsburg, June 2006. Springer.
5. Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, August 1997.
6. Mark Herbster and Manfred Warmuth. Tracking the best expert. Machine Learning, 32(2):151-178, 1998. Earlier version in 12th ICML, 1995.
7. Mark Herbster and Manfred K. Warmuth. Tracking the best linear predictor. Journal of Machine Learning Research, 1:281-309, 2001.
8. N. Littlestone and M. K. Warmuth. The weighted majority algorithm. Inform. Com-put., 108(2):212-261, 1994.
9. A. W. Marshall and I. Olkin. Inequalities: Theory of Majorization and its Applications. Academic Press, 1979.
10. R. Rockafellar. Convex Analysis. Princeton University Press, 1970.
11. K. Tsuda, G. Rätsch, and M. K. Warmuth. Matrix exponentiated gradient updates for on-line learning and Bregman projections. Journal of Machine Learning Research, 6:995-1018, June 2005.
12. Eiji Takimoto and Manfred K. Warmuth. Path kernels and multiplicative updates. Journal of Machine Learning Research, 4:773-818, 2003.
13. Manfred K. Warmuth and Dima Kuzmin. Online variance minimization. In Proceedings of the 19th Annual Conference on Learning Theory (COLT 06), Pittsburg, June 2006. Springer.