In real world applications of supervised learning methods, training and test sets are often sampled from the distinct distributions and we must resort to domain adaptation techniques. One special class of techniques is Covariate Shift Adaptation, which allows practitioners to obtain good generalization performance in the distribution of interest when domains differ only by the marginal distribution of features. Traditionally, Covariate Shift Adaptation is implemented using Importance Weighting which may fail in high-dimensional settings due to small Effective Sample Sizes (ESS). In this paper, we propose (i) a connection between ESS, high-dimensional settings and generalization bounds and (ii) a simple, general and theoretically sound approach to combine feature selection and Covariate Shift Adaptation. The new approach yields good performance with improved ESS.

本文研究了在监督学习中，训练和测试数据集经常被从不同的分布中抽样，因此需要进行领域适应技术，本文重点探讨了如何在协变量偏移适应的情况下，使用有效样本数、数据维度和泛化能力来建立一种统一的理论，并证明了降维或特征选择可以提高有效样本量，并支持在协变量偏移适应之前进行降维处理。

样本有效大小、维数和协变量转移适应中的泛化