Kernel Feature Selection via Conditional Covariance Minimization

Jianbo Chen; Mitchell Stern; Martin J. Wainwright; Michael I. Jordan

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arXiv · 1707.01164

Kernel Feature Selection via Conditional Covariance Minimization

Jianbo Chen·Mitchell Stern·Martin J. Wainwright·Michael I. Jordan

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Abstract

We propose a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response. Building on past work in kernel dimension reduction, we show how to perform feature selection via a constrained optimization problem involving the trace of the conditional covariance operator. We prove various consistency results for this procedure, and also demonstrate that our method compares favorably with other state-of-the-art algorithms on a variety of synthetic and real data sets.

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Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I. Jordan. 2017-07-04. Kernel Feature Selection via Conditional Covariance Minimization. https://arxiv.org/abs/1707.01164

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