NeurIPS 2017poster122 citations
Kernel Feature Selection via Conditional Covariance Minimization
Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael I Jordan
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.
BibTeX
@inproceedings{NIPS2017_b7fede84,
author = {Chen, Jianbo and Stern, Mitchell and Wainwright, Martin J and Jordan, Michael I},
booktitle = {Advances in Neural Information Processing Systems},
editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Kernel Feature Selection via Conditional Covariance Minimization},
url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/b7fede84c2be02ccb9c77107956560eb-Paper.pdf},
volume = {30},
year = {2017}
}