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2015
ACM

Direct conditional probability density estimation with sparse feature selection

3 years 9 months ago
Direct conditional probability density estimation with sparse feature selection
Regression is a fundamental problem in statistical data analysis, which aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional probability density is multi-modal, asymmetric, and heteroscedastic. To overcome this limitation, various estimators of conditional densities themselves have been developed, and a kernel-based approach called least-squares conditional density estimation (LS-CDE) was demonstrated to be promising. However, LS-CDE still suffers from large estimation error if input contains many irrelevant features. In this paper, we therefore propose an extension of LS-CDE called sparse additive CDE (SA-CDE), which allows automatic feature selection in CDE. SA-CDE applies kernel LS-CDE to each input feature in an additive manner and penalizes the whole solution by a group-sparse regularizer. We also give a subgradient-based optimization method for SA-CDE training that scales well to high-dimensional large d...
Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
Added 14 Apr 2016
Updated 14 Apr 2016
Type Journal
Year 2015
Where ML
Authors Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
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