  We introduce a framework for filtering features that employs the
Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence
between the features and the labels. The key idea is that good features should
maximise such dependence. Feature selection for various supervised learning
problems (including classification and regression) is unified under this
framework, and the solutions can be approximated using a backward-elimination
algorithm. We demonstrate the usefulness of our method on both artificial and
real world datasets.
