Covariate shift is a situation of supervised learning where
training and test inputs follow different distributions while
the functional relation remains unchanged. A common
approach to compensating for the bias caused by covariate
shift is to reweight the training samples according to the
importance, which is the ratio of test and training densities.
In this paper, we address the problem of estimating the
importance from samples and propose a novel method that
allows us to directly estimate the importance without going
through a 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 abundant
unlabeled samples. We demonstrate through experiments
that the proposed method is computationally more efficient
than existing approaches with competitive accuracy.
By: Yuta Tsuboi, Hisashi Kashima, Shohei Hido, Steffen Bickel, and Masashi Sugiyama
Published in: Proceedings of SIAM Data Mining in 2008
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