Transductive Domain Adaptation with Affinity Learning
Le Shu, Longin Jan Latecki · 2015
We study the problem of domain adaptation, which aims to adapt the classifiers trained on a labeled source domain to an unlabeled target domain. We propose a novel method to solve domain adaptation task in a transductive setting. The proposed method bridges the distribution gap between source domain and target domain through affinity learning. It exploits the existence of a subset of data points in target domain which distribute similarly to the data points in the source domain. These data points act as the bridge that facilitates the data similarities propagation across domains. We also propose to control the relative importance of intra- and inter-domain similarities to boost the similarity propagation. In our approach, we first construct the similarity matrix which encodes both the intra- and inter-domain similarities. We then learn the true similarities among data points in joint manifold using graph diffusion.