Domain adaption is to transform the source and target domain data into a certain space through a certain transformation, so that the probability distribution of the transformed data is as close as possible. The domain adaption algorithm based on Maximum Mean Difference (MMD) Maximization and Reproducing Kernel Hilbert Space (RKHS) subspace transformation is the current main algorithm for domain adaption, in which the RKHS subspace transformation is determined by MMD of the transformed source and target domain data. However, MMD has inherent defects in theory. The probability distributions of two different random variables will not change after subtracting their respective mean values, but their MMD becomes zero. A reasonable method should be that the MMD of the source and target domain data with the same label should be as small as possible after RKHS subspace transformation. However, the labels of target domain data are unknown and there is no way to model according to this criterion. In this paper, a domain adaption algorithm based on source dictionary regularized RKHS subspace learning is proposed, in which the source domain data are used as a dictionary, and the target domain data are approximated by the sparse coding of the dictionary. That is to say, in the process of RKHS subspace transformation, the target domain data are distributed around the mostly relevant source domain data. In this way, the proposed algorithm indirectly achieves the MMD of the source and target domain data with the same label after RKHS subspace transformation. So far there has been no similar work reported in the published academic papers. The experimental results presented in this paper show that the proposed algorithm outperforms 5 other state-of-the-art domain adaption algorithms on 5 commonly used datasets.

域自适应是将源域和目标域数据通过一定的变换变换到一定的空间中，使变换后的数据的概率分布尽可能接近。基于最大均值差（MMD）最大化和再现核希尔伯特空间（RKHS）子空间变换的域自适应算法是当前域自适应的主要算法，其中RKHS子空间变换由变换后的源域和目标域数据的MMD决定. 然而，MMD在理论上存在固有缺陷。两个不同随机变量的概率分布在减去各自的平均值后不会发生变化，但它们的MMD变为零。一个合理的方法应该是经过RKHS子空间变换后，具有​​相同标签的源域和目标域数据的MMD尽可能小。然而，目标域数据的标签是未知的，没有办法根据这个标准进行建模。本文提出了一种基于源字典正则化RKHS子空间学习的域自适应算法，将源域数据作为字典，通过字典的稀疏编码来逼近目标域数据。也就是说，在 RKHS 子空间变换的过程中，目标域数据分布在最相关的源域数据周围。这样，本文算法间接实现了经过RKHS子空间变换后具有相同标签的源域和目标域数据的MMD。迄今为止，已发表的学术论文中还没有报道类似的工作。本文提出的实验结果表明，所提出的算法在 5 个常用数据集上的性能优于其他 5 种最先进的域适应算法。