PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Statistical Convergence of Kernel CCA
K Fukumizu, F Bach and Arthur Gretton
In: NIPS 2005, Dec. 5 - Dec. 10, Vancouver, Canada.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1696
Deposited By:Arthur Gretton
Deposited On:28 November 2005