SERAPH: semi-supervised metric learning paradigm with hyper sparsity (情報論的学習理論と機械学習)

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概要

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• We consider the problem of learning a distance metric from very limited side information with unlabeled data. The proposed SERAPH (SEmi-supervised metRic leArning Paradigm with Hyper sparsity) is a direct and substantially more natural approach for semi-supervised metric learning, since the supervised and unsupervised parts are based on a unified information-theoretic framework. Unlike other semi-supervised extensions, the unsupervised part of SERAPH can extract further side information from unlabeled data by interacting with the supervised part positively. SERAPH involves both the sparsity of posterior distributions over unobserved weak labels and the sparsity of induced projection matrices, which we call the hyper sparsity. The resulting optimization is solved by an EM-like scheme, where M-Step is convex, and E-Step has an analytical solution. Experimental results show that SERAPH compares favorably with existing metric learning algorithms based on weak labels.

• 2011-06-13

著者

• Sugiyama Masashi

  Department Of Computer Science Tokyo Institute Of Technology

• NIU Gang

  Department of Computer Science, Tokyo Institute of Technology

• Niu Gang

  Department Of Computer Science Tokyo Institute Of Technology

• Yamada Makoto

  Department Of Chemistry And Biomolecular Science Toho University

• DAI Bo

  Institute of Automation, Chinese Academy Of Sciences

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

• Dai Bo

  Institute Of Automation Chinese Academy Of Sciences

• Sugiyama Masashi

  Department of Applied Chemistry, Yamanashi University

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