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2008

Kernel Measures of Independence for non-iid Data

8 years 11 months ago
Kernel Measures of Independence for non-iid Data
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.
Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smo
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Xinhua Zhang, Le Song, Arthur Gretton, Alex J. Smola
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