NDLI: Direct importance estimation for covariate shift adaptation

Content Provider	Springer Nature Link
Author	Sugiyama, Masashi Suzuki, Taiji Nakajima, Shinichi Kashima, Hisashi Bünau, Paul Kawanabe, Motoaki
Copyright Year	2008
Abstract	A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likelihood estimation are no longer consistent—weighted variants according to the ratio of test and training input densities are consistent. Therefore, accurately estimating the density ratio, called the importance, is one of the key issues in covariate shift adaptation. A naive approach to this task is to first estimate training and test input densities separately and then estimate the importance by taking the ratio of the estimated densities. However, this naive approach tends to perform poorly since density estimation is a hard task particularly in high dimensional cases. In this paper, we propose a direct importance estimation method that does not involve density estimation. Our method is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized. Furthermore, we give rigorous mathematical proofs for the convergence of the proposed algorithm. Simulations illustrate the usefulness of our approach.
Starting Page	699
Ending Page	746
Page Count	48
File Format	PDF
ISSN	00203157
Journal	Annals of the Institute of Statistical Mathematics
Volume Number	60
Issue Number	4
e-ISSN	15729052
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-08-30
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Covariate shift Importance sampling Model misspecification Kullback–Leibler divergence Likelihood cross validation Statistics for Business/Economics/Mathematical Finance/Insurance Statistics
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability

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