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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
448-454

Multi-view clustering based on structured tensor learning

Li Xinyu1
Kang Kehan1
Peng Chong2
1. School of Computer Science & Technology, Qingdao University, Qingdao Shandong 266071, China
2. School of Computer Science & Technology, Ocean University of China, Qingdao Shandong 266100, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0278

Abstract

Multi view clustering methods have become a research hotspot with the increasing diversity of data acquisition techniques. However, most clustering methods underestimate the impact of noise and complementary structural information of the data. Moreover, they often ignore the reverse guidance of clustering results on the optimization process of low rank tensors. To address these issues, this paper proposed a multi-view clustering method based on structured tensor learning(MCSTL). First, it further denoised the initial representation tensor to enhance its accuracy and robustness. At the same time, it complementarily learnt local structure, global structure, and high-order correlation across different views, which improved the consistency between the representation tensor and the intrinsic cluster structure of the original data. Then, it learnt a unified feature matrix from the affinity matrix of cross-view information fusion, and utilized the implicit clustering structure information within it to inversely guide the optimization process of the representation tensor. Lastly, it imposed an orthogonal constraint on the feature matrix, which provided soft label information of the data and allows for a direct clustering interpretation of the model. The experimental results show the MCSTL performs well in all six clustering evaluation metrics, with 27 out of 30 metrics reaching the optimal level, fully verifying the effectiveness and superiority of the MCSTL method.

Foundation Support

山东省高等学校青年创新团队资助项目(2022KJ149)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0278
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Algorithm Research & Explore
Pages: 448-454
Serial Number: 1001-3695(2025)02-017-0448-07

Publish History

[2025-02-05] Printed Article

Cite This Article

李心雨, 康可涵, 彭冲. 基于结构化张量学习的多视图聚类 [J]. 计算机应用研究, 2025, 42 (2): 448-454. (Li Xinyu, Kang Kehan, Peng Chong. Multi-view clustering based on structured tensor learning [J]. Application Research of Computers, 2025, 42 (2): 448-454. )

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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