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Journal Article

Feature Selection via Dependence Maximization

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84953

Smola,  A
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83946

Gretton,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  K
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-B808-7
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.