Information and Media Technologies
Online ISSN : 1881-0896
ISSN-L : 1881-0896
Media (processing) and Interaction
Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
Yuta TsuboiHisashi KashimaShohei HidoSteffen BickelMasashi Sugiyama
Author information
JOURNAL FREE ACCESS

2009 Volume 4 Issue 2 Pages 529-546

Details
Abstract

Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common approach to compensating for the bias caused by covariate shift is to reweight the loss function according to the importance, which is the ratio of test and training densities. We propose a novel method that allows us to directly estimate the importance from samples without going through the hard task of density estimation. An advantage of the proposed method is that the computation time is nearly independent of the number of test input samples, which is highly beneficial in recent applications with large numbers of unlabeled samples. We demonstrate through experiments that the proposed method is computationally more efficient than existing approaches with comparable accuracy. We also describe a promising result for large-scale covariate shift adaptation in a natural language processing task.

Related papers from these authors
© 2009 by Information Processing Society of Japan
Previous article Next article
feedback
Top