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Statistical Convergence of Kernel CCA

2006

Conference Paper

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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.

Author(s): Fukumizu, K. and Bach, F. and Gretton, A.
Book Title: Advances in neural information processing systems 18
Journal: Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference
Pages: 387-394
Year: 2006
Month: May
Day: 0
Editors: Weiss, Y. , B. Sch{\"o}lkopf, J. Platt
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Address: Cambridge, MA, USA
Digital: 0
Event Name: Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005)
Event Place: Vancouver, BC, Canada
ISBN: 0-262-23253-7
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{3775,
  title = {Statistical Convergence of Kernel CCA},
  author = {Fukumizu, K. and Bach, F. and Gretton, A.},
  journal = {Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference},
  booktitle = {Advances in neural information processing systems 18},
  pages = {387-394},
  editors = {Weiss, Y. , B. Sch{\"o}lkopf, J. Platt},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = may,
  year = {2006},
  month_numeric = {5}
}