title: Multi-Stage Multi-Task Feature Learning.
abstract: Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an [Formula: see text]-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.
authors.affiliation: State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation, Tsinghua University, Beijing 100084, China gph08@mails.tsinghua.edu.cn , zcs@mail.tsinghua.edu.cn.
authors.forename: Pinghua
authors.initials: P
authors.lastname: Gong
authors.affiliation: Computer Science and Engineering, Center for Evolutionary Medicine and Informatics The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA jieping.ye@asu.edu.
authors.forename: Jieping
authors.initials: J
authors.lastname: Ye
authors.affiliation: State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation, Tsinghua University, Beijing 100084, China gph08@mails.tsinghua.edu.cn , zcs@mail.tsinghua.edu.cn.
authors.forename: Changshui
authors.initials: C
authors.lastname: Zhang