Volume 30 Issue 1
Jan.  2021
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WANG Xuesong, ZHAO Jijuan, CHENG Yuhu, YU Qiang. Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM[J]. Chinese Journal of Electronics, 2021, 30(1): 109-118. doi: 10.1049/cje.2020.11.008
Citation: WANG Xuesong, ZHAO Jijuan, CHENG Yuhu, YU Qiang. Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM[J]. Chinese Journal of Electronics, 2021, 30(1): 109-118. doi: 10.1049/cje.2020.11.008

Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM

doi: 10.1049/cje.2020.11.008
Funds:

National Natural Science Foundation of China 61772532

National Natural Science Foundation of China 61976215

More Information
  • Author Bio:

    WANG Xuesong  received the Ph.D. degree from China University of Mining and Technology in 2002. She is currently a professor in the School of Information and Control Engineering, China University of Mining and Technology. Her main research interests include machine learning and pattern recognition. (Email: wangxuesongcumt@163.com)

    ZHAO Jijuan  received the M.S. degree from China University of Mining and Technology in 2019. Her main research interest is transfer learning. (Email: 1379709024@qq.com)

    YU Qiang  received the Ph.D. degree from University of Bundeswehr Muenchen, Munich, Germany in 2012. He is currently an associate professor in the School of Information and Control Engineering, China University of Mining and Technology. His main research interests include intelligent learning and systems. (Email: yuqiangcumt@163.com)

  • Corresponding author: CHENG Yuhu  (corresponding author) received the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2005. He is currently a professor in the School of Information and Control Engineering, China University of Mining and Technology. His main research interests include machine learning and intelligent system. (Email: chengyuhu@163.com)
  • Received Date: 2019-05-20
  • Accepted Date: 2019-07-10
  • Publish Date: 2021-01-01
  • In the problem of unsupervised domain adaption Extreme learning machine (ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive ability. Experiments on six different types of datasets show that the proposed model has higher cross-domain classification accuracy.
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