Abstract
Unsupervised domain adaptation, which aims to classify a target domain correctly only using a labeled source domain, has achieved promising performance yet remains a challenging problem. Most traditional methods focus on exploiting either geometric or statistical characteristics to reduce domain shifts. To take advantage of both sides, in this paper, we propose a unified framework incorporating both the geometric and statistical characteristics by adopting the non-convex Schatten p-norm and graph Laplacian constraints to preserve global and local structure information and constructing marginal and conditional distribution minimization terms to reduce the distribution shifts. Moreover, a classification error term on the source domain is embedded into the objective function to increase the discriminability. The proposed method has been evaluated on six datasets and the experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods. The MATLAB code of our method will be publicly available at https://github.com/HeyouChang/unsupervised-domain-adaptation.
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Acknowledgements
This work was partially supported by the NSFC under Grant Nos. 61806098, 61976118, 61972212, 61603192 and 61772568, the Natural Science Foundation of Jiangsu Province under Grant No. BK20190089 and BK20180142, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant Nos. 18KJB520029 and 17KJB520020, Nanjing Xiaozhuang University under Grant Nos. 2017NXY49.
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Chang, H., Zhang, F., Gao, G. et al. Graph-structure constraint and Schatten p-norm-based unsupervised domain adaptation for image classification. J Ambient Intell Human Comput 13, 5137–5149 (2022). https://doi.org/10.1007/s12652-020-02350-y
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DOI: https://doi.org/10.1007/s12652-020-02350-y