Paper Info
Title
TSK Fuzzy System for Multi-View Data Discovery Underlying Label Relaxation and Cross-Rule & Cross-View Sparsity Regularizations
Abstract
Industry 4.0 places special emphasis on the use of intelligent models to discover patterns in data. In this article, we propose a novel Takagi-Sugeno-Kang (TSK) fuzzy system with low model complexity for multiview data pattern discovery. Compared with the classic TSK fuzzy systems, the proposed one has three merits: First, we introduce a transformation matrix to relax the strict binary label matrix of the training set so that the margins between classes become more discriminative. Second, we introduce two kinds of sparsity regularizations, i.e., cross-rule and cross-view, to reduce indiscriminative fuzzy rules and consequent parameters so that the model complexity is significantly reduced. Third, we introduce the alternating direction method of multipliers to optimize the objective function so that we have compact closed-form solutions in each iteration. Extensive experiments on different kinds of multiview image datasets indicate the promising performance for data pattern discovery with low model complexity.
Year
DOI
Venue
2021
10.1109/TII.2020.3007174
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Cross-rule and cross-view sparsity,data discovery,Industry 4.0,label relaxation,Takagi–Sugeno–Kang (TSK) fuzzy system
Journal
17
Issue
ISSN
Citations 
5
1551-3203
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Kaijian Xia1458.07
Yuanpeng Zhang233.48
Yizhang Jiang338227.24
pengjiang qian4305.48
Jiancheng Dong523.76
Hongsheng Yin6144.75
Raymond F Muzic751.42