Multi-task feature learning

author: Andreas Argyriou, École Centrale Paris
published: Feb. 25, 2007,   recorded: July 2006,   views: 489
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Description

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 well-known 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.

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