Unsupervised multilayer fuzzy neural networks for image clustering

Wang，Yifan1,2; Ishibuchi，Hisao2,3; Er，Meng Joo4; Zhu，Jihua1

2023-04-01

10.1016/j.ins.2022.11.149

INFORMATION SCIENCES   影响因子和分区

0020-0255

1872-6291

Currently, labelling a large number of images is still a very challenging task. To tackle the problem of unlabelled data, unsupervised learning has been proposed. Among many unsupervised learning algorithms, K-means is the most popular algorithm. However, in a low-dimensional space, fuzzy c-means, which is more robust and less sensitive to initialization, has several advantages over K-means clustering. On the other hand, stacked convolutional pooling structures and manifold representation play pivotal roles in image clustering. In this paper, we propose an unsupervised multilayer fuzzy neural network for image clustering that unifies fuzzy systems, multilayer convolutional structures and manifold representation. The main contributions are as follows. First, we extend fuzzy systems to unsupervised tasks by introducing manifold representation, which expands the applications of fuzzy systems. Next, we propose the idea of using only a small number of attributes to compute firing strengths. This is implemented to prevent the firing strengths from falling to zero. Finally, randomly generated convolutional weights are used to extract features, which is a good choice for data without labels. It is demonstrated on a wide range of image datasets that the proposed approach is competitive with existing fuzzy and nonfuzzy clustering algorithms.

Fuzzy clustering Fuzzy neural networks Image clustering

SCI ; EI

英语

通讯

Key Research and Development Program of Shaanxi Province["2021GXLH-Z-097","2021GY-025"] ; National Natural Science Foundation of China[61876075] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531] ; Fundamental Research Funds for the Central Universities[3132019344] ; Leading Scholar Grant, Dalian Maritime University[00253007]

Computer Science

Computer Science, Information Systems

WOS:000900836600002

ELSEVIER SCIENCE INC

20225013247020

Convolution ; Fuzzy inference ; Fuzzy systems ; K-means clustering ; Learning algorithms ; Multilayer neural networks ; Multilayers ; Unsupervised learning

Information Theory and Signal Processing:716.1 ; Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory:721.1 ; Artificial Intelligence:723.4 ; Expert Systems:723.4.1 ; Machine Learning:723.4.2 ; Information Sources and Analysis:903.1 ; Systems Science:961

COMPUTER SCIENCE

2-s2.0-85143752296

Scopus

1.School of Software Engineering,Xi'an Jiaotong University,Xi'an,710049,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,China
4.Institute of Artificial Intelligence and Marine Robotics,College of Marine Electrical Engineering,Dalian Maritime University,Dalian,116026,China

Wang，Yifan,Ishibuchi，Hisao,Er，Meng Joo,et al. Unsupervised multilayer fuzzy neural networks for image clustering[J]. INFORMATION SCIENCES,2023,622:682-709.

Wang，Yifan,Ishibuchi，Hisao,Er，Meng Joo,&Zhu，Jihua.(2023).Unsupervised multilayer fuzzy neural networks for image clustering.INFORMATION SCIENCES,622,682-709.

Wang，Yifan,et al."Unsupervised multilayer fuzzy neural networks for image clustering".INFORMATION SCIENCES 622(2023):682-709.