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JMLR
2010

Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation

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Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
The goal of sufficient dimension reduction in supervised learning is to find the lowdimensional subspace of input features that is `sufficient' for predicting output values. In this paper, we propose a novel sufficient dimension reduction method using a squaredloss variant of mutual information as a dependency measure. We utilize 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.
Taiji Suzuki, Masashi Sugiyama
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Taiji Suzuki, Masashi Sugiyama
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