Exclusive Lasso for Multi-task Feature Selection

• Main Authors: ZHOU, Yang, JIN, Rong, HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2010
• Subjects: Document categorization; Feature selection algorithm; Optimization problems; Regularizer; State-of-the-art algorithms; Computer Sciences; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/2317; https://ink.library.smu.edu.sg/context/sis_research/article/3317/viewcontent/zhou10a.pdf
• Institution: Singapore Management University

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.