On the Consistency of Feature Selection Using Greedy Least Squares Regression
Abstract
This paper studies the feature selection problem using a greedy least squares regression algorithm. We show that under a certain irrepresentable condition on the design matrix (but independent of the sparse target), the greedy algorithm can select features consistently when the sample size approaches infinity. The condition is identical to a corresponding condition for Lasso.
Cite
Text
Zhang. "On the Consistency of Feature Selection Using Greedy Least Squares Regression." Journal of Machine Learning Research, 2009.Markdown
[Zhang. "On the Consistency of Feature Selection Using Greedy Least Squares Regression." Journal of Machine Learning Research, 2009.](https://mlanthology.org/jmlr/2009/zhang2009jmlr-consistency/)BibTeX
@article{zhang2009jmlr-consistency,
title = {{On the Consistency of Feature Selection Using Greedy Least Squares Regression}},
author = {Zhang, Tong},
journal = {Journal of Machine Learning Research},
year = {2009},
pages = {555-568},
volume = {10},
url = {https://mlanthology.org/jmlr/2009/zhang2009jmlr-consistency/}
}