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Kernel measures of independence for non-iid data

Zhang, Xinhua; Song, Le; Gretton, Arthur; Smola, Alexander


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.

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
Source: Advances in Neural Information Processing Systems 21


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