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Feature Selection via 1-Penalized Squared-Loss Mutual Information
Wittawat JITKRITTUM Hirotaka HACHIYA Masashi SUGIYAMA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2013/07/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
feature selection, 1-regularization, squared-loss mutual information, density-ratio estimation, dimensionality reduction,
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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 1-LSMI, an 1-regularization based algorithm that maximizes a squared-loss variant of mutual information between selected features and outputs. Numerical results show that 1-LSMI performs well in handling redundancy, detecting non-linear dependency, and considering feature interaction.