Domain adaptation has been a fundamental technology for transferring
knowledge from a source domain to a target domain. The key issue of domain
adaptation is how to reduce the distribution discrepancy between two domains in
a proper way such that they can be treated indifferently for learning. In this
paper, we propose a novel domain adaptation approach, which can thoroughly
explore the data distribution structure of target domain.Specifically, we
regard the samples within the same cluster in target domain as a whole rather
than individuals and assigns pseudo-labels to the target cluster by class
centroid matching. Besides, to exploit the manifold structure information of
target data more thoroughly, we further introduce a local manifold
self-learning strategy into our proposal to adaptively capture the inherent
local connectivity of target samples. An efficient iterative optimization
algorithm is designed to solve the objective function of our proposal with
theoretical convergence guarantee. In addition to unsupervised domain
adaptation, we further extend our method to the semi-supervised scenario
including both homogeneous and heterogeneous settings in a direct but elegant
way. Extensive experiments on seven benchmark datasets validate the significant
superiority of our proposal in both unsupervised and semi-supervised manners.

本文提出了一种新颖的领域自适应方法，通过聚类和伪标签匹配等技术，对减少两个领域之间的分布差异，实现了在无监督和有监督领域自适应方面的显着优势。