Title: Kernel Feature Selection via Conditional Covariance Minimization,
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.