Supervised feature selection via dependence estimation

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.

• Type: Conference
• Year: 2007
• Where: ICML
• Authors: Le Song, Alex J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo
• Keywords: Hilbert-Schmidt Independence Criterion, ICML 2007, Machine Learning, Real World Datasets, Supervised Learning Problems