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Domain Adaptation with Few Labeled Source Samples by Graph Regularization

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Abstract

Domain Adaptation aims at utilizing source data to establish an exact model for a related but different target domain. In recent years, many effective models have been proposed to propagate label information across domains. However, these models rely on large-scale labeled data in source domain and cannot handle the case where the source domain lacks label information. In this paper, we put forward a Graph Regularized Domain Adaptation (GDA) to tackle this problem. Specifically, the proposed GDA integrates graph regularization with maximum mean discrepancy (MMD). Hence GDA enables sufficient unlabeled source data to facilitate knowledge transfer by utilizing the geometric property of source domain, simultaneously, due to the embedding of MMD, GDA can reduce source and target distribution divergency to learn a generalized classifier. Experimental results validate that our GDA outperforms the traditional algorithms when there are few labeled source samples.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61671480, in part by the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) under Grant 18CX07011A, in part by the Macau Science and Technology Development Fund under Grant FDCT/189/2017/A3, and in part by the Research Committee at University of Macau under Grant MYRG2016-00123-FST and Grant MYRG2018-00136-FST.

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Authors and Affiliations

  1. College of Information and Control Engineering, China University of Petroleum (East China), Tsingtao, 266580, China

    Jinfeng Li & Weifeng Liu

  2. Faculty of Science and Technology, University of Macau, Macau, 999078, China

    Yicong Zhou

  3. School of Information Science and Engineering, Yunnan University, Kunming, 650091, China

    Dapeng Tao

  4. School of Computer Science and Technology, Shandong University, Tsingtao, 266237, China

    Liqiang Nie

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  1. Jinfeng Li
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  2. Weifeng Liu
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  3. Yicong Zhou
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  4. Dapeng Tao
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  5. Liqiang Nie
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Correspondence to Weifeng Liu.

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Li, J., Liu, W., Zhou, Y. et al. Domain Adaptation with Few Labeled Source Samples by Graph Regularization. Neural Process Lett 51, 23–39 (2020). https://doi.org/10.1007/s11063-019-10075-z

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