Conference Proceeding Article
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.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
JMLR Workshop and Conference Proceedings: 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, May 13-15, Chia Laguna Resort, Sardinia, Italy
City or Country
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, Chia Laguna Resort, Sardinia, Italy. 9, 988-995. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2317
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