Transductive Multiview Modeling With Interpretable Rules, Matrix Factorization, and Cooperative Learning
Wei Zhang, Zhaohong Deng, Jun Wang, Kup‐Sze Choi, Te Zhang, Xiaoqing Luo, Hong‐Bin Shen, Wenhao Ying, Shitong Wang
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.