Robust multi-task feature learning

7 years 11 months ago
Robust multi-task feature learning
Multi-task learning (MTL) aims to improve the performance of multiple related tasks by exploiting the intrinsic relationships among them. Recently, multi-task feature learning algorithms have received increasing attention and they have been successfully applied to many applications involving high-dimensional data. However, they assume that all tasks share a common set of features, which is too restrictive and may not hold in real-world applications, since outlier tasks often exist. In this paper, we propose a Robust MultiTask Feature Learning algorithm (rMTFL) which simultaneously captures a common set of features among relevant tasks and identifies outlier tasks. Specifically, we decompose the weight (model) matrix for all tasks into two components. We impose the well-known group Lasso penalty on row groups of the first component for capturing the shared features among relevant tasks. To simultaneously identify the outlier tasks, we impose the same group Lasso penalty but on colum...
Pinghua Gong, Jieping Ye, Changshui Zhang
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where KDD
Authors Pinghua Gong, Jieping Ye, Changshui Zhang
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