Exclusive Lasso for Multi-Task Feature Selection
Abstract
We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes co-varying variables in groups, the proposed exclusive lasso regularizer models the scenario when variables in the same group compete with each other. Analysis is presented to illustrate the properties of the proposed regularizer. We present a framework of kernel-based multi-task feature selection algorithm based on the proposed exclusive lasso regularizer. An efficient algorithm is derived to solve the related optimization problem. Experiments with document categorization show that our approach outperforms state-of-the-art algorithms for multi-task feature selection.
Cite
Text
Zhou et al. "Exclusive Lasso for Multi-Task Feature Selection." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Zhou et al. "Exclusive Lasso for Multi-Task Feature Selection." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/zhou2010aistats-exclusive/)BibTeX
@inproceedings{zhou2010aistats-exclusive,
title = {{Exclusive Lasso for Multi-Task Feature Selection}},
author = {Zhou, Yang and Jin, Rong and Hoi, Steven Chu–Hong},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {988-995},
volume = {9},
url = {https://mlanthology.org/aistats/2010/zhou2010aistats-exclusive/}
}