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Nonconvex and discriminative transfer subspace learning for unsupervised domain adaptation

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Abstract

Unsupervised transfer subspace learning is one of the challenging and important topics in domain adaptation, which aims to classify unlabeled target data by using source domain information. The traditional transfer subspace learning methods often impose low-rank constraints, i.e., trace norm, to preserve data structural information of different domains. However, trace norm is only the convex surrogate to approximate the ideal low-rank constraints and may make their solutions seriously deviate from the original optimums. In addition, the traditional methods directly use the strict labels of source domain, which is difficult to deal with label noise. To solve these problems, we propose a novel nonconvex and discriminative transfer subspace learning method named NDTSL by incorporating Schatten-p norm and soft label matrix. Specifically, Schatten-p norm can be imposed to approximate the low-rank constraints and obtain a better low-rank representation. Then, we design and adopt soft label matrix in source domain to learn a more flexible classifier and enhance the discriminative ability of target data. Besides, due to the nonconvexity of Schatten-p norm, we design an efficient alternative algorithm IALM to solve it. Finally, experimental results on several public transfer tasks demonstrate the effectiveness of NDTSL compared with several state-of-the-art methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 61922087), and the Huxiang Young Talents Program of Hunan Province (2021RC3070).

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Authors and Affiliations

  1. College of Science, National University of Defense Technology, Changsha, 410073, China

    Yueying Liu & Tingjin Luo

Authors
  1. Yueying Liu
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  2. Tingjin Luo
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Correspondence to Tingjin Luo.

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Competing interests The authors declare that they have no competing interests or financial conflicts to discloe.

Additional information

Yueying Liu received the BS degree in Information and Computing Science from Shanxi University, China in 2021. She is working toward the master degree in System Theory at National University of Defense Technology, China. Her current research interests include machine learning, optimization, and computer vision.

Tingjin Luo received his PhD degrees from National University of Defense Technology, China. He is currently an associate professor with the College of Science of the same university. He was a visiting PhD student with the University of Michigan, USA from 2015 to 2017. He has authored more than 40 papers in journals and conferences, such as the IEEE TKDE, IEEE TCYB, ACM KDD, and ICME. He has been a Program Committee member of several conferences including IJCAI, AAAI, ICPR, etc. His research interests include machine learning, optimization, data mining, and computer vision.

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Liu, Y., Luo, T. Nonconvex and discriminative transfer subspace learning for unsupervised domain adaptation. Front. Comput. Sci. 19, 192307 (2025). https://doi.org/10.1007/s11704-023-3228-0

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