Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2010
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.
Keywords
Document categorization, Feature selection algorithm, Optimization problems, Regularizer, State-of-the-art algorithms
Discipline
Computer Sciences | Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
JMLR Workshop and Conference Proceedings: 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, May 13-15, Sardinia, Italy
Volume
9
First Page
988
Last Page
995
ISSN
1532-4435
Publisher
JMLR
City or Country
Cambridge, MA
Citation
ZHOU, Yang; JIN, Rong; and HOI, Steven C. H..
Exclusive Lasso for Multi-task Feature Selection. (2010). JMLR Workshop and Conference Proceedings: 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, May 13-15, Sardinia, Italy. 9, 988-995.
Available at: https://ink.library.smu.edu.sg/sis_research/2317
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://proceedings.mlr.press/v9/zhou10a/zhou10a.pdf