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Empirical Inference Article 2012

Feature Selection via Dependence Maximization

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Empirical Inference
Karsten Borgwardt
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Empirical Inference
Arthur Gretton

We introduce a framework of 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.

Author(s): Song, L. and Smola, A. and Gretton, A. and Bedo, J. and Borgwardt, K.
Journal: Journal of Machine Learning Research
Volume: 13
Pages: 1393-1434
Year: 2012
Month: May
Day: 0
Bibtex Type: Article (article)
Digital: 0
Electronic Archiving: grant_archive
Links:

BibTex 

@article{SongSGBB2012,
  title = {Feature Selection via Dependence Maximization},
  journal = {Journal of Machine Learning Research},
  abstract = {We introduce a framework of 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.},
  volume = {13},
  pages = {1393-1434},
  month = may,
  year = {2012},
  slug = {songsgbb2012},
  author = {Song, L. and Smola, A. and Gretton, A. and Bedo, J. and Borgwardt, K.},
  month_numeric = {5}
}