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  Statistical Convergence of Kernel CCA

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D1EF-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D1F0-2
Genre: Conference Paper

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 Creators:
Fukumizu, K1, Author           
Bach, F, Author
Gretton, A1, Author           
Weiss, Editor
Y., Editor
Schölkopf, B., Editor
Platt, J., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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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 Dates: 2006-05
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 0-262-23253-7
URI: http://nips.cc/Conferences/2005/
BibTex Citekey: 3775
 Degree: -

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Title: Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005)
Place of Event: Vancouver, BC, Canada
Start-/End Date: -

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Title: Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference
Source Genre: Journal
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Affiliations:
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 387 - 394 Identifier: -