Adaptive Transductive Transfer Machines: A Pipeline for Unsupervised Domain Adaptation

Abstract

This chapter addresses the problem of transfer learning by unsupervised domain adaptation . We introduce a pipeline which is designed for the case where the joint distribution of samples and labels $P(\mathbf {X}^{src},\mathbf {Y}^{src})$ in the source domain is assumed to be different, but related to that of a target domain $P(\mathbf {X}^{ trg },\mathbf {Y}^{ trg })$, but labels $\mathbf {Y}^{ trg }$ are not available for the target set. This is a problem of Transductive Transfer Learning. In contrast to other methodologies in this book, our method combines steps that adapt both the marginal and the conditional distributions of the data.