Squared-loss Mutual Information Regularization
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概要
- 論文の詳細を見る
The information maximization principle is a useful alternative to the low-density separation principle and prefers probabilistic classifiers that maximize the mutual information (MI) between data and labels. In this paper, we propose an approach for semi-supervised learning called squared-loss mutual information (SMI) regularization, which replaces MI with a novel information measure SMI. SMI regularization is the first framework that can offer all these four properties to algorithms: analytical solution, out-of-sample and multi-class classification, and probabilistic output. As an information-theoretic framework, it is directly related to a manifold regularization, and results in learning algorithms with data-dependent risk bounds. Experiments demonstrate that SMI regularization compares favorably with existing approaches of information-theoretic regularization.
- 2012-03-05
著者
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Hachiya Hirotaka
Department Of Computer Science Tokyo Institute Of Technology
-
Niu Gang
Department Of Computer Science Tokyo Institute Of Technology
-
Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
-
DAI Bo
Department of Computer Science, Purdue University
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JITKRITTUM Wittawat
Department of Computer Science, Tokyo Institute of Technology
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Sugiyama Masashi
Department of Applied Chemistry, Yamanashi University
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