Publikationsansicht

Kernel Measures of Conditional Dependence (2008)

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.

Details der Publikation
Download http://eprints.pascal-network.org/archive/00004334/
Herausgeber MIT Press
Mitarbeiter Platt, J.C., Koller, D., Singer, Y., Roweis, S.
Archiv PASCAL EPrints (United Kingdom)
Keywords Computational, Information-Theoretic Learning with Statistics, Learning/Statistics & Optimisation, Brain Computer Interfaces, Theory & Algorithms
Typ Book Section, PeerReviewed
Verknüpfungen http://eprints.pascal-network.org/archive/00004334/01/NIPS2007-Fukumizu_4914[0].pdf