Feature Selection via Dependence Maximization
Le Song, Alex Smola, Arthur Gretton, Justin Bedo, Karsten Borgwardt; 13(47):1393−1434, 2012.
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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