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Kernel Measures of Conditional Dependence

2008

Conference Paper

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We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel dependence measures, the proposed criterion does not depend on the choice of kernel in the limit of infinite data, for a wide class of kernels. At the same time, it has a straightforward empirical estimate with good convergence behaviour. We discuss the theoretical properties of the measure, and demonstrate its application in experiments.

Author(s): Fukumizu, K. and Gretton, A. and Sun, X. and Schölkopf, B.
Book Title: Advances in neural information processing systems 20
Journal: Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007
Pages: 489-496
Year: 2008
Month: September
Day: 0
Editors: JC Platt and D Koller and Y Singer and S Roweis
Publisher: Curran

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

Address: Red Hook, NY, USA
Digital: 0
Event Name: 21st Annual Conference on Neural Information Processing Systems (NIPS 2007)
Event Place: Vancouver, BC, Canada
ISBN: 978-1-605-60352-0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4914,
  title = {Kernel Measures of Conditional Dependence},
  author = {Fukumizu, K. and Gretton, A. and Sun, X. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007},
  booktitle = {Advances in neural information processing systems 20},
  pages = {489-496},
  editors = {JC Platt and D Koller and Y Singer and S Roweis},
  publisher = {Curran},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Red Hook, NY, USA},
  month = sep,
  year = {2008},
  month_numeric = {9}
}