Feature Selection via Dependence Maximization
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.
Cite
Text
Song et al. "Feature Selection via Dependence Maximization." Journal of Machine Learning Research, 2012.Markdown
[Song et al. "Feature Selection via Dependence Maximization." Journal of Machine Learning Research, 2012.](https://mlanthology.org/jmlr/2012/song2012jmlr-feature/)BibTeX
@article{song2012jmlr-feature,
title = {{Feature Selection via Dependence Maximization}},
author = {Song, Le and Smola, Alex and Gretton, Arthur and Bedo, Justin and Borgwardt, Karsten},
journal = {Journal of Machine Learning Research},
year = {2012},
pages = {1393-1434},
volume = {13},
url = {https://mlanthology.org/jmlr/2012/song2012jmlr-feature/}
}