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A spectral regularization framework for multi-task structure learning

Argyriou, A; Pontil, M; Micchelli, CA; Ying, Y; (2009) A spectral regularization framework for multi-task structure learning. In: Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference.

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Abstract

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 effficiently by an alternating minimization algorithm. In addition, we provide a necessary and sufficient 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.

Type:Proceedings paper
Title:A spectral regularization framework for multi-task structure learning
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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