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  Kernel Measures of Independence for Non-IID Data

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C485-C Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C486-A
Genre: Conference Paper

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 Creators:
Zhang, X, Author
Song L, Gretton, A1, Author           
Smola, A, Author
Koller, Editor
D., Editor
Schuurmans, D., Editor
Bengio, Y., Editor
Bottou, L., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 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.

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 Dates: 2009-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: ISBN: 978-1-605-60949-2
URI: http://nips.cc/Conferences/2008/
BibTex Citekey: 5465
 Degree: -

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Title: Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)
Place of Event: Vancouver, BC, Canada
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Title: Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008
Source Genre: Journal
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Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1937 - 1944 Identifier: -