基于张量表示的域适配的迁移学习中特征表示方法

本文提出一种新的基于张量表示的域适配迁移学习中的特征表示方法，即融合联合域对齐和适配正则化的基于张量表示的迁移学习特征表示方法.当源域和目标域差异很大时，仅将源域对齐潜在共享空间，会造成数据扭曲过大.为缓解此问题，本文方法提出联合域对齐，即源域和目标域同时对齐共享子空间.并且本文方法将适配正则化引入张量表示空间求解.本文适配正则化包括动态分布对齐和图适配，以缩小域间分布差异和保留样本间流行一致性.最后融合联合域对齐，动态分布对齐和图适配，通过联合优化求解获得共享子空间表示.几个公共的跨域数据集上的大量实验结果表明了本文方法优于其它主流的迁移学习方法，验证了本文方法的有效性.

A Novel Feature Representation Based on Tensor and Domain Adaption for Transfer Learning

A novel feature representation based on tensor and domain adaption for transfer learning is proposed, which combines joint domain alignment and adaptation regularization.When the difference between the source domain and the target domain is very large, only aligning the source domain to the potential shared subspace will result in too much data distortion.To alleviate this problem, this paper proposes joint domain alignment, which aligns the source domain and the target domain to the potential shared subspaces simultaneously.Furthermore, the adaption regularization is introduced into the subspace learning based on tensor.In the proposed method, adaptation regularization includes dynamic distribution alignment and graph adaptation to reduce the distribution differences among different domains and preserve the manifold consistency.Finally, the joint domain alignment, dynamic distributed alignment and graph adaptation are fused, and the joint optimization is utilized to obtain the feature representation.Extensive experiments on several common cross-domain datasets show that the proposed method outperforms the state-of-the-art on the tasks of transfer learning and the effectiveness of the proposed method is verified.