In this paper, we investigate the unsupervised domain transfer learning in which there is no label in the target samples while the source samples are all labeled. In our approach the target and source samples are transferred to a new domain and each target sample is constructed by from the linear combination of the source samples in the new transformed domain. The low-rank and sparse constraints are imposed on the reconstruction coefficient matrix which maintains the local and global structure of the samples in the transferred domain. In this paper, the information content of the reconstruction coefficient matrix is utilized in order to consider the discriminative ability of the source samples. Here, we utilize the max-margin classifier in which the kernel matrix is defined using the reconstruction coefficient matrix. To evaluate the proposed method, it is applied on Office and Caltech-256 datasets. The experimental results show that our proposed approach is performed better than the state-of-the-art approaches.