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Machine Learning in Non-Stationary EnvironmentsIntroduction to Covariate Shift Adaptation$
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Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Masashi Sugiyama and Motoaki Kawanabe

Print publication date: 2012

Print ISBN-13: 9780262017091

Published to MIT Press Scholarship Online: September 2013

DOI: 10.7551/mitpress/9780262017091.001.0001

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Direct Density-Ratio Estimation with Dimensionality Reduction

Direct Density-Ratio Estimation with Dimensionality Reduction

Chapter:
(p.103) 5 Direct Density-Ratio Estimation with Dimensionality Reduction
Source:
Machine Learning in Non-Stationary Environments
Author(s):

Masashi Sugiyama

Motoaki Kawanabe

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262017091.003.0005

This chapter discusses a dimensionality reduction scheme for density-ratio estimation, called direct density-ratio estimation with dimensionality reduction (D3; pronounced as “D-cube”). The basic idea of D3 is to find a low-dimensional subspace in which training and test densities are significantly different, and estimate the density ratio only in this subspace. A supervised dimensionality reduction technique called local Fisher discriminant analysis (LFDA) is employed for identifying such a subspace. The usefulness of the D3 approach is illustrated through numerical experiments.

Keywords:   dimensionality reduction, called direct density-ratio estimation, subspace, local Fisher discriminant analysis

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