Publication Type

Conference Proceeding Article

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.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

JMLR Workshop and Conference Proceedings: 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, May 13-15, Chia Laguna Resort, Sardinia, Italy

Volume

9

First Page

988

Last Page

995

Publisher

JMLR

City or Country

Cambridge, MA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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