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  Feature Selection via Dependence Maximization

Song, L., Smola, A., Gretton, A., Bedo, J., & Borgwardt, K. (2012). Feature Selection via Dependence Maximization. Journal of Machine Learning Research, 13, 1393-1434.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B808-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-19F0-4
Genre: Journal Article

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 Creators:
Song, L, Author
Smola, A, Author              
Gretton, A, Author              
Bedo, J, Author
Borgwardt, K1, 2, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_2528696              

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 Abstract: We introduce a framework for feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highly dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our feature selector works well in practice.

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 Dates: 2012-03
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: SongSGBB2012
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Title: Journal of Machine Learning Research
Source Genre: Journal
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Pages: - Volume / Issue: 13 Sequence Number: - Start / End Page: 1393 - 1434 Identifier: -