Multi-label multiple kernel learning

Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations

Abstract

We present a multi-label multiple kernel learning (MKL) formulation in which the data are embedded into a low-dimensional space directed by the instancelabel correlations encoded into a hypergraph. We formulate the problem in the kernel-induced feature space and propose to learn the kernel matrix as a linear combination of a given collection of kernel matrices in the MKL framework. The proposed learning formulation leads to a non-smooth min-max problem, which can be cast into a semi-infinite linear program (SILP). We further propose an approximate formulation with a guaranteed error bound which involves an unconstrained convex optimization problem. In addition, we show that the objective function of the approximate formulation is differentiable with Lipschitz continuous gradient, and hence existing methods can be employed to compute the optimal solution efficiently. We apply the proposed formulation to the automated annotation of Drosophila gene expression pattern images, and promising results have been reported in comparison with representative algorithms.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages777-784
Number of pages8
StatePublished - Dec 1 2009
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: Dec 8 2008Dec 11 2008

Publication series

NameAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Other

Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
CountryCanada
CityVancouver, BC
Period12/8/0812/11/08

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Ji, S., Sun, L., Jin, R., & Ye, J. (2009). Multi-label multiple kernel learning. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 777-784). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).