Kullback-Leibler importance estimation procedure for covariate shift adaptation

抄録

A situation where training and test samples follow different imput 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 problem particularly in high dimensional cases. In this paper, we propose a direct importance estimation method that does not require density estimates. Our method is equipped with a natural cross validation procedure and hence tuning parameters such as the kernel width can be objectively optimized. Simulations illustrate the usefulness of our approach.