Supervised feature selection via dependence estimation

This work introduces a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion as a measure of dependence between the features and the labels and demonstrates the usefulness of the method on both artificial and real world datasets.

Abstract

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.