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Issue No.07 - July (2010 vol.22)

pp: 957-968

Tsuyoshi Kato , Ochanomizu University, Tokyo

Hisahi Kashima , IBM Research, Yamato

Masashi Sugiyama , Tokyo Institute of Technology, Tokyo

Kiyoshi Asai , The University of Tokyo, Chiba

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.142

ABSTRACT

When we have several related tasks, solving them simultaneously has been shown to be more effective than solving them individually. This approach is called multitask learning (MTL). In this paper, we propose a novel MTL algorithm. Our method controls the relatedness among the tasks locally, so all pairs of related tasks are guaranteed to have similar solutions. We apply the above idea to support vector machines and show that the optimization problem can be cast as a second-order cone program, which is convex and can be solved efficiently. The usefulness of our approach is demonstrated in ordinal regression, link prediction, and collaborative filtering, each of which can be formulated as a structured multitask problem.

INDEX TERMS

Multitask learning, second-order cone programming, ordinal regression, link prediction, collaborative filtering.

CITATION

Tsuyoshi Kato, Hisahi Kashima, Masashi Sugiyama, Kiyoshi Asai, "Conic Programming for Multitask Learning",

*IEEE Transactions on Knowledge & Data Engineering*, vol.22, no. 7, pp. 957-968, July 2010, doi:10.1109/TKDE.2009.142REFERENCES