Feature Selection via l_1-Penalized Squared-Loss Mutual Information
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概要
- 論文の詳細を見る
Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose l_1-LSMI, an l_1-regularization based algorithm that maximizes a squared-loss variant of mutual information between selected features and outputs. Numerical results show that l_1-LSMI performs well in handling redundancy, detecting non-linear dependency, and considering feature interaction.
- 2012-03-05
著者
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Hachiya Hirotaka
Department Of Computer Science Tokyo Institute Of Technology
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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JITKRITTUM Wittawat
Department of Computer Science, Tokyo Institute of Technology
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