Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

Statistical Convergence of Kernel CCA


Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Fukumizu, K., Bach, F., & Gretton, A. (2006). Statistical Convergence of Kernel CCA. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference (pp. 387-394). Cambridge, MA, USA: MIT Press.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D1EF-8
While kernel canonical correlation analysis (kernel CCA) has been applied in many problems, the asymptotic convergence of the functions estimated from a finite sample to the true functions has not yet been established. This paper gives a rigorous proof of the statistical convergence of kernel CCA and a related method (NOCCO), which provides a theoretical justification for these methods. The result also gives a sufficient condition on the decay of the regularization coefficient in the methods to ensure convergence.