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Conference Paper

Kernel Measures of Independence for Non-IID Data

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

Song L, Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zhang, X., Song L, Gretton, A., & Smola, A. (2009). Kernel Measures of Independence for Non-IID Data. Advances in neural information processing systems 21: 22nd Annual Conference on Neural Information Processing Systems 2008, 1937-1944.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C485-C
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.