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Kernel methods for measuring independence

Gretton, Arthur; Herbrich, Ralf; Smola, Alexander; Bousquet, Olivier; Schoelkopf, Bernhard

Description

We introduce two new functionals, the constrained covariance and the kernel mutual information, to measure the degree of independence of random variables. These quantities are both based on the covariance between functions of the random variables in reproducing kernel Hilbert spaces (RKHSs). We prove that when the RKHSs are universal, both functionals are zero if and only if the random variables are pairwise independent. We also show that the kernel mutual information is an upper bound...[Show more]

CollectionsANU Research Publications
Date published: 2005-12
Type: Journal article
URI: http://hdl.handle.net/10440/299
http://digitalcollections.anu.edu.au/handle/10440/299
Source: Journal of Machine Learning Research

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