The Lasso, Persistence, and Cross-Validation
Abstract
During the last fifteen years, the lasso procedure has been the target of a substantial amount of theoretical and applied research. Correspondingly, many results are known about its behavior for a fixed or optimally chosen smoothing parameter (given up to unknown constants). Much less, however, is known about the lasso’s behavior when the smoothing parameter is chosen in a data dependent way. To this end, we give the first result about the risk consistency of lasso when the smoothing parameter is chosen via cross-validation. We consider the high-dimensional setting wherein the number of predictors p=n^α, α>0 grows with the number of observations.
Cite
Text
Homrighausen and McDonald. "The Lasso, Persistence, and Cross-Validation." International Conference on Machine Learning, 2013.Markdown
[Homrighausen and McDonald. "The Lasso, Persistence, and Cross-Validation." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/homrighausen2013icml-lasso/)BibTeX
@inproceedings{homrighausen2013icml-lasso,
title = {{The Lasso, Persistence, and Cross-Validation}},
author = {Homrighausen, Darren and McDonald, Daniel},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {1031-1039},
volume = {28},
url = {https://mlanthology.org/icml/2013/homrighausen2013icml-lasso/}
}