Dynamic classifier approximation for unsupervised domain adaptation

In recent years, domain adaptation (DA) method has been proposed to solve the problem of domain shift between the training set and test set. However, most feature-based unsupervised domain adaptation methods have a weakness of strict requirement on the transformation matrix and they are not integrated with classifier. Therefore, this paper proposes a novel dynamic classifier approximation (DCA) method for unsupervised domain adaptation. Specifically, the proposed DCA, which combines classifier method to relax the requirements of transformation matrix, learns domain invariant features from structure and data level. 1) At the structure level, the low-rank representation, sparse representation and manifold learning are combined to preserve the structure information of data in the original feature space. 2) At the data level, distribution alignments are adopted to minimize the distribution difference of the source and target domains in the common subspace, which is helpful to reduce the negative impact caused by domain shift. In addition, for learning the common subspace more flexibly, a dynamic classifier is introduced to reduce the strict requirements on projection matrix. This paper carries out a large number of experiments on four data sets, and the experimental results show that the proposed method is superior to many of the latest domain adaptation methods, which can indicate its effectiveness.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In recent years, domain adaptation (DA) methods have been proposed to address the issue of domain shift between training and test sets. However, most feature-based unsupervised domain adaptation methods have strict requirements on the transformation matrix and lack integration with classifiers. Therefore, this paper introduces a novel dynamic classifier approximation (DCA) method for unsupervised domain adaptation. The proposed DCA combines classifier methods to relax the requirements on the transformation matrix and learns domain invariant features from both the structure and data levels. Experimental results on four datasets demonstrate the superiority of the proposed method over many state-of-the-art domain adaptation methods, indicating its effectiveness.