PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search
M Suigyama, M Yamada, Paul Buenau, T Suzuki, T Kanamori and Motoaki Kawanabe
Neural Networks Volume 24, Number 2, pp. 183-198, 2011.

Abstract

Methods for directly estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, and feature selection. In this paper, we develop a new method which incorporates dimensionality reduction into a direct density-ratio estimation procedure. Our key idea is to find a low-dimensional subspace in which densities are significantly different and perform density-ratio estimation only in this subspace. The proposed method, D3-LHSS (Direct Density-ratio estimation with Dimensionality reduction via Least-squares Hetero-distributional Subspace Search), is shown to overcome the limitation of baseline methods.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9464
Deposited By:Paul Buenau
Deposited On:16 March 2012