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Multi-task feature learning

Argyriou, A; Evgeniou, T; Pontil, M; (2007) Multi-task feature learning. In: (pp. pp. 41-48).

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We present a method for learning a low-dimensional representation which is shared across a set of multiple related tasks. The method builds upon the wellknown 1-norm regularization problem using a new regularizer which controls the number of learned features common for all the tasks. We show that this problem is equivalent to a convex optimization problem and develop an iterative algorithm for solving it. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the latter step we learn commonacross- tasks representations and in the former step we learn task-specific functions using these representations. We report experiments on a simulated and a real data set which demonstrate that the proposed method dramatically improves the performance relative to learning each task independently. Our algorithm can also be used, as a special case, to simply select - not learn - a few common features across the tasks.

Type: Proceedings paper
Title: Multi-task feature learning
ISBN-13: 9780262195683
URI: http://discovery.ucl.ac.uk/id/eprint/175501
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