基于K-means聚类的子结构相关适配迁移学习方法

域漂移严重影响了传统机器学习方法的性能，现有的领域自适应方法主要通过全局、类级或样本级分布匹配自适应地调整跨域表示。但全局匹配和类级匹配过于粗糙会导致自适应不足，而样本级匹配受到噪声的影响可能导致过度自适应。基于此，提出了一种基于K均值（K-means）聚类的子结构相关适配（SCOAD）迁移学习算法，首先通过K-means聚类分别获得源域和目标域的多个子域，其次寻求子域中心二阶统计量的匹配，最后利用子域内结构对目标域样本进行分类。该方法在传统方法的基础上进一步提高了源域与目标域之间知识迁移的性能。在常用迁移学习数据集上的实验结果表明了所提方法的有效性。

Substructure correlation adaptation transfer learning method based on K-means clustering

Domain drifts severely affect the performance of traditional machine learning methods, and existing domain adaptive methods are mainly represented by adaptive adjustment cross-domain through global, class-level, or sample-level distribution adaptation.However, too coarse global matching and class-level matching can lead to insufficient adaptation, and sample-level adaptation to noise can lead to excessive adaptation.A substructure correlation adaptation (SCOAD) transfer learning algorithm based on K-means clustering was proposed.Firstly, multiple subdomains of the source domain and the target domain were obtained by K-means clustering.Then, the matching of the second-order statistics of the subdomain center was sought.Finally, the target domain samples were classified by using the subdomain structure.The proposed method approach further improves the performance of knowledge transfer between the source and target domains on top of the traditional approach.Experimental results on common transfer learning datasets show the effectiveness of the proposed method.