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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. Retrieved from http://jmlr.csail.mit.edu/papers/v13/song12a.html.

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 Creators:
Song, L, Author
Smola, A1, Author           
Gretton, A2, Author           
Bedo, J, Author
Borgwardt, K1, Author           
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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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: Issued
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 Identifiers: URI: http://jmlr.csail.mit.edu/papers/v13/song12a.html
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: -