PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Spectral Regularization Framework for Multi-Task Structure Learning
Andreas Argyriou, Charles Micchelli, Massimiliano Pontil and Yiming Ying
In: NIPS 2007, Dec 2007, Vancouver, Canada.


Learning the common structure shared by a set of supervised tasks is an important practical and theoretical problem. Knowledge of this structure may lead to better generalization performance on the tasks and may also facilitate learning new tasks. We propose a framework for solving this problem, which is based on regularization with spectral functions of matrices. This class of regularization problems exhibits appealing computational properties and can be optimized efciently by an alternating minimization algorithm. In addition, we provide a necessary and sufcient condition for convexity of the regularizer. We analyze concrete examples of the framework, which are equivalent to regularization with Lp matrix norms. Experiments on two real data sets indicate that the algorithm scales well with the number of tasks and improves on state of the art statistical performance.

EPrint Type:Conference or Workshop Item (Spotlight)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3779
Deposited By:Massimiliano Pontil
Deposited On:22 February 2008