PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Hilbert Space Embedding for Distributions
Alex Smola, Arthur Gretton, Le Song and Bernhard Schölkopf
In: ALT 2007, 01 Oct - 04 Oct 2007, Sendai, Japan.

Abstract

We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.

EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:3128
Deposited By:Arthur Gretton
Deposited On:21 December 2007

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