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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 11

Multi-view clustering with self-supervised learning guided by multi-angle semantic labels

Liu Yuan1,2
An Junxiu1,2
Yang Linwang1,2
1. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610000, China
2. Key Laboratory of Manufacturing Industry Chain Collaboration & Information Technology Support, Sichuan Province, Chengdu 610000, China
DOI: 10.19734/j.issn.1001-3695.2024.04.0082

Abstract

Multi-view clustering aims to explore the feature information of objects from multiple perspectives to obtain accurate clustering results. However, existing research often fails to handle the information conflicts that arise during view fusion and does not fully utilize the complementary information between multiple views. To address these issues, this paper proposes a self-supervised multi-view clustering model guided by multi-angle semantic labels. The model first maps the latent representations of each view to independent low-dimensional feature spaces, focusing on optimizing the consistency between views in one space to maintain the local structure of the feature space and the relative relationships between samples. At the same time, in another space, clustering information is directly extracted from the view level to capture richer and more diverse semantic features. Finally, pseudo-labels generated from multi-angle semantic features guide the clustering assignment at the object level, achieving collaborative optimization of the two representations. Extensive experimental results demonstrate that this approach can comprehensively explore both common and complementary information in multi-view data and exhibit good clustering performance. Moreover, compared to other methods, this approach has advantages in scenarios with a larger number of views.

Foundation Support

国家社会科学基金资助项目(22BXW048)
四川省重点实验室开放基金资助项目(2024-ScL-MC&I-001)
成都市科技重点研发支撑计划项目(2022-YF05-00454-SN)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.04.0082
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 11

Publish History

[2024-07-30] Accepted Paper

Cite This Article

柳源, 安俊秀, 杨林旺. 多角度语义标签引导的自监督多视图聚类 [J]. 计算机应用研究, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0082. (Liu Yuan, An Junxiu, Yang Linwang. Multi-view clustering with self-supervised learning guided by multi-angle semantic labels [J]. Application Research of Computers, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.04.0082. )

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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