Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (2): 403-412.DOI: 10.3778/j.issn.1673-9418.2008064

• Artificial Intelligence • Previous Articles     Next Articles

Unsupervised Heterogeneous Domain Adaptation with Fuzzy Rule Learning

SUN Wu1, DENG Zhaohong1,2,3,+(), LOU Qiongdan1, GU Xin4, WANG Shitong1   

  1. 1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
    2. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai 200433, China
    3. Zhangjiang Lab, Shanghai 200120, China
    4. Jiangsu North Huguang Opto-Electronics Co., Ltd., Wuxi, Jiangsu 214000, China
  • Received:2020-08-20 Revised:2020-11-03 Online:2022-02-01 Published:2020-11-19
  • About author:SUN Wu, born in 1995, M.S. candidate. His research interest is interpretability artificial inte-lligence.
    DENG Zhaohong, born in 1981, professor, Ph.D. supervisor, distinguished member of CCF. His research interests include interpretability and un-certainty artificial intelligence and its applications.
    LOU Qiongdan, born in 1995, Ph.D. candidate. Her research interests include interpretability and uncertainty artificial intelligence and pattern recognition.
    GU Xin, born in 1979, Ph.D., senior engineer.His research interests include pattern recognition, artificial intelligence, image processing technology and its application.
    WANG Shitong, born in 1964, professor, Ph.D. supervisor. His research interests include artificial intelligence, pattern recognition, etc.
  • Supported by:
    National Natural Science Foundation of China(61772239);Municipal Science and Technology Major Project of “Basic Transformation and Application Research of Brain and Brain-Like Intelligence” in Shanghai(2018SHZDZX01)


孙武1, 邓赵红1,2,3,+(), 娄琼丹1, 顾鑫4, 王士同1   

  1. 1.江南大学 人工智能与计算机学院,江苏 无锡 214122
    2.复旦大学 计算神经科学与类脑智能教育部重点实验室,上海 200433
    3.张江实验室,上海 200120
    4.江苏北方湖光光电有限公司,江苏 无锡 214000
  • 通讯作者: + E-mail:
  • 作者简介:孙武(1995—),男,江苏兴化人,硕士研究生,主要研究方向为可解释人工智能。
  • 基金资助:


Heterogeneous domain adaptation is a technique that uses the knowledge of source domain to model the target domain. The source domain and the target domain are semantically related, but their feature spaces are different. Among existing heterogeneous domain adaptive methods, most of them belong to semi-supervised methods, which require some labeled samples in the target domain. However, this kind of dataset is rare in many heter-ogeneous adaptive tasks. In order to solve the above problem, this paper proposes a new unsupervised heter-ogeneous domain adaptive algorithm based on fuzzy rule learning. On the one hand, by introducing the TSK fuzzy system, the proposed method learns two feature transformation matrices corresponding to the source domain and the target domain respectively. By learning two feature transformation matrices, the source domain and the target domain are projected into a common feature subspace. On the other, in order to reduce the information loss caused by feature transformation, the proposed algorithm adopts a variety of information preservation strategies and maximizes the correlation between the transformed source domain data and target domain data. Through experi-ments on domain adaptive datasets, the results show that the proposed algorithm has certain advantages over the existing heterogeneous domain adaptive methods.

Key words: fuzzy rule learning, TSK fuzzy system, information preserving, heterogeneous domain adaptation



关键词: 模糊规则学习, TSK模糊系统, 信息保持, 异构领域自适应

CLC Number: