Kernel independent component analysis

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 4, Issue , 2003, Pages 876-879

  Bach, Francis R ^a   Jordan, Michael I ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTER SIMULATION; PROBLEM SOLVING; STATISTICAL METHODS;

HILBERT SPACE;

INDEPENDENT COMPONENT ANALYSIS;

EID: 0141632724     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. S. Amari, A. Cichocki, and H. H. Yang. A new learning algorithm for blind signal separation. In Adv. in NIPS, 8, 1996.
2. T. W. Anderson. An Introduction to Multivariate Statistical Analysis. Wiley & Sons, 1984.
3. F. R. Bach and M. I. Jordan. Kernel independent component analysis. J. of Machine Learning Research, 3:1-48, 2002.
4. F. R. Bach and M. I. Jordan. Tree-dependent component analysis. In Proc. UAI, 2002.
5. A. J. Bell and T. J. Sejnowski. An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6):1129-1159, 1995.
6. P. J. Bickel, C. A. J. Klaassen, Y. Ritov, and J. A. Wellner. Efficient and Adaptive Estimation for Semiparametric Models. Springer-Verlag, 1998.
7. J.-F. Cardoso. High-order contrasts for independent component analysis. Neural Computation, 11(1):157-192, 1999.
8. A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis. Wiley & Sons, 2001.
9. T.-W. Lee, M. Girolami, and T. J. Sejnowski. Independent component analysis using an extended Infomax algorithm for mixed sub-gaussian and super-gaussian sources. Neural Computation, 11(2):417-441, 1999.
10. B. Schölkopf and A. J. Smola. Learning with Kernels. MIT Press, 2001.