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K-Nearest Neighbor Based Local Distribution Alignment

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13394))

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Abstract

When massive labeled data are unavailable, domain adaptation can transfer knowledge from a different source domain. Many recent domain adaptation methods merely focus on extracting domain-invariant features via minimizing the global distribution divergence between domains while ignoring local distribution alignment. In order to solve the problem of incomplete distribution alignment, we propose a K-nearest neighbors based local distribution alignment method, where Maximum Mean Discrepancy (MMD) is adopted as the transfer loss function to reduce the global distribution discrepancy, and then a K-nearest neighbors based transfer loss function is also devised to minimize the local distribution difference for the complete alignment of source and target domain. The proposed method contributes to avoid the dilemma of incomplete alignment in MMD by local distribution alignment and improve its recognition accuracy. Experiments on multiple transfer learning datasets show that the proposed method performs comparatively well.

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Author information

Authors and Affiliations

  1. Wuhan University of Science and Technology, Wuhan, 430070, China

    Yang Tian & Bo Li

  2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan University of Sciences and Technology, Wuhan, 430070, China

    Yang Tian & Bo Li

Authors
  1. Yang Tian
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  2. Bo Li
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Corresponding author

Correspondence to Bo Li .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Tian, Y., Li, B. (2022). K-Nearest Neighbor Based Local Distribution Alignment. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_41

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  • DOI: https://doi.org/10.1007/978-3-031-13829-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13828-7

  • Online ISBN: 978-3-031-13829-4

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