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Spotlight
Multi-Stage Multi-Task Feature Learning
Pinghua Gong · Jieping Ye · Changshui Zhang

Wed Dec 05 12:00 PM -- 12:04 PM (PST) @ Harveys Convention Center Floor, CC
in Spotlight Session 6 »
Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an $\ell_0$-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.

Author Information

Pinghua Gong (Tsinghua University)
Jieping Ye (Arizona State University)
Changshui Zhang (Tsinghua University)

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