PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Measuring Statistical Dependence with Hilbert-Schmidt Norms
Arthur Gretton, Olivier Bousquet, Alex Smola and Bernhard Schölkopf
ALT05, Springer Lecture Notes in Computer Science Volume 3734, 2005. ISSN 3-540-29242-X

Abstract

We propose an independence criterion based on the eigenspectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:1628
Deposited By:Arthur Gretton
Deposited On:28 November 2005