PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Measures of Conditional Dependence
K. Fukumizu, A. Gretton, X. Sun and B. Schölkopf
In: Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference (2008) MIT Press , Cambridge, MA, USA , pp. 489-496.

Abstract

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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Brain Computer Interfaces
Theory & Algorithms
ID Code:4334
Deposited By:Bernhard Schölkopf
Deposited On:13 March 2009