Multi-stage multi-task feature learning

Pinghua Gong; Jieping Ye; Changshui Zhang

Multi-stage multi-task feature learning

Pinghua Gong, Jieping Ye, Changshui Zhang

Computing, Informatics and Decision Systems Engineering, School of (CIDSE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

45 Scopus citations

Abstract

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 l₀-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.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 25
Subtitle of host publication	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Pages	1988-1996
Number of pages	9
State	Published - Dec 1 2012
Event	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 - Lake Tahoe, NV, United States Duration: Dec 3 2012 → Dec 6 2012

Publication series

Name	Advances in Neural Information Processing Systems
Volume	3
ISSN (Print)	1049-5258

Other

Other	26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012
Country	United States
City	Lake Tahoe, NV
Period	12/3/12 → 12/6/12

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

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Cite this

Gong, P., Ye, J., & Zhang, C. (2012). Multi-stage multi-task feature learning. In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012 (pp. 1988-1996). (Advances in Neural Information Processing Systems; Vol. 3).

Multi-stage multi-task feature learning. / Gong, Pinghua; Ye, Jieping; Zhang, Changshui.

Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012. 2012. p. 1988-1996 (Advances in Neural Information Processing Systems; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Gong, P, Ye, J & Zhang, C 2012, Multi-stage multi-task feature learning. in Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012. Advances in Neural Information Processing Systems, vol. 3, pp. 1988-1996, 26th Annual Conference on Neural Information Processing Systems 2012, NIPS 2012, Lake Tahoe, NV, United States, 12/3/12.

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