PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Measures of Independence for non-iid Data
Xinhua Zhang, Le Song, Arthur Gretton and Alex Smola
In: NIPS 2008, 8-12 Dec 2008, Vancouver, Canada.

Abstract

Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures using undirected graphical models and comparing the Hilbert space embeddings of distributions. We apply this new criterion to independent component analysis and sequence clustering.

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EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:5227
Deposited By:Xinhua Zhang
Deposited On:24 March 2009