Kernel Measures of Independence for Non-IID Data

Zhang, X; Song, L; Gretton, A; Smola, A

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Kernel Measures of Independence for Non-IID Data

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

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https://papers.nips.cc/paper/3440-kernel-measures-of-independence-for-non-iid-data.pdf
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Citation

Zhang, X., Song, L., Gretton, A., & Smola, A. (2009). Kernel Measures of Independence for Non-IID Data. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 1937-1944). Red Hook, NY, USA: Curran.

Cite as: https://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.