Abstract: Because training data tend to be outdated,training data and test data have different feature distributions in most cases.Therefore,when using the source domain information,it is necessary to minimize the difference of feature distributions in different fields.Features represented by tensor can maintain the intrinsic structure information of high-dimensional spatial data.Naive tensor subspace learning is a domain adaptation method for tensor features,but it has high complexity and can not achieve good knowledge transfer effect.For this reason,this paper proposed a domain adaptation algorithm based on tensor decomposition,namely tensor train subspace learning and tensor ring subspace lear-ning,and the main ideas of the two methods are similar.Firstly,the features of source domain and target domain are coded into tensor.By using the tensor decomposition,the tensor of features is decomposed into a series of third-order tensors to represent the subspace.Then,the features of source domain and target domain are mapped into subspace successively.Finally,the feature tensor is reshaped into matrix form.Based on the mapped features of source domain training model and the mapped feature of target domain,the task in new domain is completed.Experiments show that the tensor train subspace learning and the tensor ring subspace learning are improved in terms of accuracy and running time for unsupervised image classification.Compared with the naive tensor subspace learning,the accuracy of the tensor train subspace learning and the tensor ring subspace learning is improved by 1.68% and 2.08% respectively,the running time is also reduced significantly,and the complexity of the algorithm is smaller.Experimental results show that the domain adaptation algorithm based on tensor decomposition can reduce the difference between source domain and target domain,and realize the reuse of knowledge between different domains.

Key words: Domain adaptation, Image classification, Subspace learning, Tensor decomposition, Transfer learning