人工知能学会第二種研究会資料
Online ISSN : 2436-5556
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
Taiji SuzukiMasashi Sugiyama
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研究報告書・技術報告書 フリー

2009 年 2009 巻 DMSM-A901 号 p. 05-

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The goal of sufficient dimension reduction in supervised learning is to find the low-dimensional subspace of input features that is 'sufficient' for predicting output values. In this paper, we propose a novel sufficient dimension reduction method using a squared-loss variant of mutual information as a dependency measure. We derive an analytic approximator of squared-loss mutual information based on density ratio estimation, which is shown to possess suitable convergence properties. We then develop a natural gradient algorithm for sufficient subspace search. Numerical experiments show that the proposed method compares favorably with existing dimension reduction approaches.

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