Exclusive Lasso for Multi-task Feature Selection

We propose a novel group regularization which we call exclusive lasso. Unlike the group lasso regularizer that assumes covarying 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-theart algorithms for multi-task feature selection.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Yang Zhou, Rong Jin, Steven C. H. Hoi
• Keywords: Exclusive Lasso, Exclusive Lasso Regularizer, JMLR 2010, Multi-task Feature Selection