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Algorithm Research & Explore
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102-107,115

Multi-view clustering based on similarity graph projection learning

Zhao Weihao1
Lin Haoshen2
Cao Chuanjie1
Yang Xiaojun1
1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2. Unit 96901 of the PLA, Beijing 100094, China

Abstract

With the diversified development of data sources, multi-view clustering has become a research hotspot. Most algorithms focus too much on using graph structure to seek consistent representation, but ignore how to learn the graph structure itself. In addition, some methods are usually optimized based on fixed views. In order to solve these problems, this paper proposed a multi-view clustering algorithm based on similarity graph projection learning(MCSGP), which effectively fused the global structure information and local potential information into a consensus graph by using the projection graph, rather than only pursuing the consistency of each view with the consensus graph. By imposing a rank constraint on the graph Laplacian matrix of the consensus graph matrix, this algorithm could naturally divide the data points into the required number of clusters. In the experiments on two artificial datasets and seven real datasets, the MCSGP algorithm shows excellent clustering effect on artificial data sets. At the same time, in the real datasets involving 21 indicators, 17 indicators reach the optimal level, which fully proves the superior performance of the proposed algorithm.

Foundation Support

广东省面上自然科学基金资助项目(2021A1515011141)
国防重点实验室开放基金资助项目
国家自然科学基金青年资助项目(61904041)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.05.0195
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Algorithm Research & Explore
Pages: 102-107,115
Serial Number: 1001-3695(2024)01-016-0102-06

Publish History

[2023-07-21] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

赵伟豪, 林浩申, 曹传杰, 等. 基于相似图投影学习的多视图聚类 [J]. 计算机应用研究, 2024, 41 (1): 102-107,115. (Zhao Weihao, Lin Haoshen, Cao Chuanjie, et al. Multi-view clustering based on similarity graph projection learning [J]. Application Research of Computers, 2024, 41 (1): 102-107,115. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.


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