Kernel Measures of Independence for non-iid Data

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Xinhua Zhang, Le Song, Arthur Gretton, Alex Smola


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 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.