2008年12月
Direct importance estimation for covariate shift adaptation
ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
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- 巻
- 60
- 号
- 4
- 開始ページ
- 699
- 終了ページ
- 746
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1007/s10463-008-0197-x
- 出版者・発行元
- SPRINGER HEIDELBERG
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.
- リンク情報
- ID情報
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- DOI : 10.1007/s10463-008-0197-x
- ISSN : 0020-3157
- eISSN : 1572-9052
- Web of Science ID : WOS:000260635300002