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Statistical Convergence of Kernel CCA

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83923

Fukumizu,  K
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

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

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Zitation

Fukumizu, K., Bach, F., & Gretton, A. (2006). Statistical Convergence of Kernel CCA. Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference, 387-394.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D1EF-8
Zusammenfassung
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.