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.