Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
Abstract
A new approach to regression regularization called the Pairwise Elastic Net is proposed. Like the Elastic Net, it simultaneously performs automatic variable selection and continuous shrinkage. In addition, the Pairwise Elastic Net encourages the grouping of strongly correlated predictors based on a pairwise similarity measure. We give examples of how the Pairwise Elastic Net can be used to achieve the objectives of Ridge regression, the Lasso, the Elastic Net, and Group Lasso. Finally, we present a coordinate descent algorithm to solve the Pairwise Elastic Net.
Cite
Text
Lorbert et al. "Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Lorbert et al. "Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/lorbert2010aistats-exploiting/)BibTeX
@inproceedings{lorbert2010aistats-exploiting,
title = {{Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net}},
author = {Lorbert, Alexander and Eis, David and Kostina, Victoria and Blei, David and Ramadge, Peter},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {477-484},
volume = {9},
url = {https://mlanthology.org/aistats/2010/lorbert2010aistats-exploiting/}
}