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.
Original language | English |
---|---|
Pages (from-to) | 1393-1434 |
Number of pages | 42 |
Journal | Journal of Machine Learning Research |
Volume | 13 |
Publication status | Published - May 2012 |