A Regularization Approach to Nonlinear Variable Selection
Abstract
In this paper we consider a regularization approach to variable selection when the regression function depends nonlinearly on a few input variables. The proposed method is based on a regularized least square estimator penalizing large values of the partial derivatives. An efficient iterative procedure is proposed to solve the underlying variational problem, and its convergence is proved. The empirical properties of the obtained estimator are tested both for prediction and variable selection. The algorithm compares favorably to more standard ridge regression and L1 regularization schemes.
Cite
Text
Rosasco et al. "A Regularization Approach to Nonlinear Variable Selection." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Rosasco et al. "A Regularization Approach to Nonlinear Variable Selection." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/rosasco2010aistats-regularization/)BibTeX
@inproceedings{rosasco2010aistats-regularization,
title = {{A Regularization Approach to Nonlinear Variable Selection}},
author = {Rosasco, Lorenzo and Santoro, Matteo and Mosci, Sofia and Verri, Alessandro and Villa, Silvia},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {653-660},
volume = {9},
url = {https://mlanthology.org/aistats/2010/rosasco2010aistats-regularization/}
}