Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation
نویسندگان
چکیده
منابع مشابه
Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
The goal of sufficient dimension reduction in supervised learning is to find the low-dimensional subspace of input features that contains all of the information about the output values that the input features possess. In this letter, we propose a novel sufficient dimension-reduction method using a squared-loss variant of mutual information as a dependency measure. We apply a density-ratio estim...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2013
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_00407