Transductive Multiview Modeling With Interpretable Rules, Matrix Factorization, and Cooperative Learning

Wei Zhang, Zhaohong Deng, Jun Wang, Kup Sze Choi, Te Zhang, Xiaoqing Luo, Hongbin Shen, Wenhao Ying, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Multiview fuzzy systems aim to deal with fuzzy modeling in multiview scenarios effectively and to obtain the interpretable model through multiview learning. However, current studies of multiview fuzzy systems still face several challenges, one of which is how to achieve efficient collaboration between multiple views when there are few labeled data. To address this challenge, this article explores a novel transductive multiview fuzzy modeling method. The dependency on labeled data is reduced by integrating transductive learning into the fuzzy model to simultaneously learn both the model and the labels using a novel learning criterion. Matrix factorization is incorporated to further improve the performance of the fuzzy model. In addition, collaborative learning between multiple views is used to enhance the robustness of the model. The experimental results indicate that the proposed method is highly competitive with other multiview learning methods.

Original languageEnglish
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Collaboratively learning
  • fuzzy system
  • Fuzzy systems
  • Matrix decomposition
  • matrix factorization
  • Optimization
  • Robustness
  • Support vector machines
  • Training
  • Training data
  • transductive multiview learning.

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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