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.

中文翻译：

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类质心匹配和局部流形自学习的领域适应

领域适应已成为将知识从源领域转移到目标领域的一项基本技术。域适应的关键问题是如何以适当的方式减少两个域之间的分布差异，以便可以对它们进行无差异的学习。在本文中，我们提出了一种新颖的域自适应方法，可以彻底探索目标域的数据分布结构。具体而言，我们将目标域中同一群集中的样本视为一个整体而不是单个样本，并通过类质心匹配将伪标签分配给目标群集。此外，为了更全面地利用目标数据的多种结构信息，我们进一步在建议中引入了局部流形自学习策略，以自适应地捕获目标样本的固有本地连通性。设计了一种有效的迭代优化算法，以理论上的收敛保证来解决我们建议的目标函数。除了无监督域自适应以外，我们还以一种直接而优雅的方式将我们的方法进一步扩展到半监督场景，包括同构和异构设置。在七个基准数据集上进行的大量实验以无监督和半监督的方式证明了我们的建议的显着优势。我们将我们的方法以一种直接而优雅的方式进一步扩展到包括同质和异质设置的半监督场景。在七个基准数据集上进行的大量实验以无监督和半监督的方式证明了我们的建议的显着优势。我们将我们的方法以一种直接而优雅的方式进一步扩展到包括同质和异质设置的半监督场景。在七个基准数据集上进行的大量实验以无监督和半监督的方式证明了我们的建议的显着优势。