TY - GEN
T1 - A convex formulation for learning shared structures from multiple tasks
AU - Chen, Jianhui
AU - Tang, Lei
AU - Liu, Jun
AU - Ye, Jieping
PY - 2009
Y1 - 2009
N2 - Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. In this paper, we consider the problem of learning shared structures from multiple related tasks. We present an improved formulation (iASO) for multi-task learning based on the non-convex alternating structure optimization (ASO) algorithm, in which all tasks are related by a shared feature representation. We convert iASO, a non-convex formulation, into a relaxed convex one, which is, however, not scalable to large data sets due to its complex constraints. We propose an alternating optimization (cASO) algorithm which solves the convex relaxation efficiently, and further show that cASO converges to a global optimum. In addition, we present a theoretical condition, under which cASO can find a globally optimal solution to iASO. Experiments on several benchmark data sets confirm our theoretical analysis.
AB - Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. In this paper, we consider the problem of learning shared structures from multiple related tasks. We present an improved formulation (iASO) for multi-task learning based on the non-convex alternating structure optimization (ASO) algorithm, in which all tasks are related by a shared feature representation. We convert iASO, a non-convex formulation, into a relaxed convex one, which is, however, not scalable to large data sets due to its complex constraints. We propose an alternating optimization (cASO) algorithm which solves the convex relaxation efficiently, and further show that cASO converges to a global optimum. In addition, we present a theoretical condition, under which cASO can find a globally optimal solution to iASO. Experiments on several benchmark data sets confirm our theoretical analysis.
UR - http://www.scopus.com/inward/record.url?scp=71149094644&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=71149094644&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:71149094644
SN - 9781605585161
T3 - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
SP - 137
EP - 144
BT - Proceedings of the 26th International Conference On Machine Learning, ICML 2009
T2 - 26th International Conference On Machine Learning, ICML 2009
Y2 - 14 June 2009 through 18 June 2009
ER -