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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 4
1024-1029

Robust self-adaptived symmetric nonnegative matrix factorization clustering algorithm

Gao Haiyan1,2
Liu Wanjin1
Huang Hengjun1,2
1. School of Statistics, Lanzhou University of Finance & Economics, Lanzhou 730020, China
2. Key Laboratory of Digital Economy & Social Computing Science, Lanzhou 730020, China
DOI: 10.19734/j.issn.1001-3695.2022.08.0414

Abstract

As a graph-based clustering method, SNMF can more naturally capture the clustering structure embedded in the graph representation and obtain better clustering results on linear and nonlinear manifolds, but it is sensitive to the initialization of variables. In addition, standard SNMF algorithm uses the sum of error squares to measure the quality of decomposition, which is sensitive to noise and outliers. In order to solve these problems, this paper proposed a novel robust self-adaptived symmetric nonnegative matrix factorization for clustering(RS3NMF) from the perspective of ensemble learning. The L2, 1 norm-based RS3NMF model alleviated the noise and outliers influence, and kept the rotation invariance property to improve the model robustness. Meanwhile, using the sensitivity of SNMF to initialization features, it gradually enhanced the clustering performance, without relying on any additional information. It adopted alternating iteration method to optimize, and ensured the convergence of the objective function value. Extensive experimental results show that the proposed RS3NMF outperforms other state-of-the-art algorithms in terms of both clustering performance and robustness.

Foundation Support

国家社会科学基金资助项目(19XTJ002,20XTJ005)
中央引导地方科技发展资助项目(GSK215115)
甘肃省软科学专项资助项目(20CX9ZA047)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0414
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 4
Section: Algorithm Research & Explore
Pages: 1024-1029
Serial Number: 1001-3695(2023)04-011-1024-06

Publish History

[2022-12-07] Accepted Paper
[2023-04-05] Printed Article

Cite This Article

高海燕, 刘万金, 黄恒君. 鲁棒自适应对称非负矩阵分解聚类算法 [J]. 计算机应用研究, 2023, 40 (4): 1024-1029. (Gao Haiyan, Liu Wanjin, Huang Hengjun. Robust self-adaptived symmetric nonnegative matrix factorization clustering algorithm [J]. Application Research of Computers, 2023, 40 (4): 1024-1029. )

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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