Transfer Domain Class Clustering for Unsupervised Domain Adaptation

  • Yunxin Fan
  • Gang Yan
  • Shuang Li
  • Shiji Song
  • Wei Wang
  • Xinping Peng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 482)


In this paper, we propose a transfer domain class clustering (TDCC) algorithm to address the unsupervised domain adaptation problem, in which the training data (source domain) and the test data (target domain) follow different distributions. TDCC aims to derive new feature representations for source and target in a latent subspace to simultaneously reduce the distribution distance between two domains, which helps transfer the source knowledge to the target domain effectively, and enhance the class discriminativeness of data as much as possible by minimizing the intra-class variations, which can benefit the final classification a lot. The effectiveness of TDCC is verified by comprehensive experiments on several cross-domain datasets, and the results demonstrate that TDCC is superior to the competitive algorithms.


Feature learning Distribution adaptation Domain adaptation Transfer learning 



This research is supported by the CRRC Major Scientific Projects under Grant No. 2106CKZ206-1 and National Key R&D Program under Grant No. 2016YFB1200203.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Yunxin Fan
    • 1
  • Gang Yan
    • 1
  • Shuang Li
    • 2
  • Shiji Song
    • 2
  • Wei Wang
    • 1
  • Xinping Peng
    • 1
  1. 1.The State Key Laboratory of Heavy Duty AC Drive Electric Locomotive Systems IntegrationHunanChina
  2. 2.Tsinghua UniversityBeijingChina

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