计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (2): 403-412.DOI: 10.3778/j.issn.1673-9418.2008064

• 人工智能 • 上一篇    下一篇

基于模糊规则学习的无监督异构领域自适应

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
  • 收稿日期:2020-08-20 修回日期:2020-11-03 出版日期:2022-02-01 发布日期:2020-11-19
  • 通讯作者: + E-mail: dengzhaohong@jiangnan.edu.cn
  • 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。
    邓赵红(1981—),男,安徽蒙城人,教授,博士生导师,CCF杰出会员,主要研究方向为可解释和不确定性人工智能及其应用。
    娄琼丹(1995—),女,江苏邳州人,博士研究生,主要研究方向为可解释和不确定性人工智能与模式识别。
    顾鑫(1979—),男,江苏张家港人,博士,高级工程师,主要研究方向为模式识别、人工智能、图像处理技术研究与应用。
    王士同(1964—),男,江苏扬州人,教授,博士生导师,主要研究方向为人工智能、模式识别等。
  • 基金资助:
    国家自然科学基金面上项目(61772239);上海市“脑与类脑智能基础转化应用研究”市级重大科技专项(2018SHZDZX01)

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)

摘要:

异构领域自适应是一种借助源域知识为语义相关但特征空间不同的目标域建模的技术。现有的异构领域自适应方法大多属于半监督方法,这些方法要求目标域中存在一部分已标记样本,然而这种数据集在很多异构领域自适应任务中是稀缺的。为了解决上述问题,提出了一种新的基于模糊规则学习的无监督异构领域自适应算法。一方面,该方法基于TSK模糊系统的规则学习分别对源域和目标域进行特征学习,通过学习两个特征变换矩阵将源域和目标域投影到一个公共特征子空间;另一方面,为了减少因特征变换所造成的信息损失,该算法采取了多种信息保持策略,并且最大化公共特征子空间中源域数据和目标域数据之间的相关性。通过在几个真实领域自适应数据集上进行实验,验证了所提算法相对于现有的异构领域自适应方法具有一定的优越性。

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

Abstract:

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

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