PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Kernel Test of Statistical Dependence
Arthur Gretton, kenji fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf and Alex Smola
In: NIPS 2007, 03 Dec - 06 Dec 2007, Vancouver Canada.

Abstract

Although kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically significant dependence. We provide a novel test of the independence hypothesis for one particular kernel independence measure, the Hilbert-Schmidt independence criterion (HSIC). The resulting test costs O(m^2), where m is the sample size. We demonstrate that this test outperforms established contingency table-based tests. Finally, we show the HSIC test also applies to text (and to structured data more generally), for which no other independence test presently exists.

EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:3142
Deposited By:Arthur Gretton
Deposited On:21 December 2007