Feature Selection via Dependence Maximization

Le Song; Alex Smola; Arthur Gretton; Justin Bedo; Karsten Borgwardt

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.

Feature Selection via Dependence Maximization

Abstract