Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
72-75,158

Low rank subspace clustering algorithm based on accurate estimation for matrix rank function

Liu Mingming1,2
Yang Yuancan2
Yang Yanbo2
Zhang Haiyan1
1. School of Intelligent Manufacturing, Jiangsu Vocational Institute of Architectural Technology, Xuzhou Jiangsu 221116, China
2. School of Computer Science & Technology, China University of Mining & Technology, Xuzhou Jiangsu 221116, China
DOI: 10.19734/j.issn.1001-3695.2023.04.0219

Abstract

Traditional subspace clustering methods usually replace the matrix rank function by the matrix kernel norm to recover the original low rank matrices. However, in the process of minimizing the matrix kernel norm, these algorithms pay too much attention to the calculation of the large singular values of the matrix, resulting in inaccurate estimation of the matrix rank. To this end, this paper analyzed the long tail distribution of matrix singular values and proposed a low rank subspace clustering model based on truncated Schatten-p norm. The proposed model fitted the long tail distribution of matrix singular va-lues well and toke full account of the contribution of small singular values to the process of low rank matrix recovery. The mo-del could make full use of small singular values to fit the long tail distribution of matrix singular values, ultimately achieved an accurate estimation of matrix rank function and improved the performance of subspace clustering. The experimental results show that, compared with the WNNM-LRR and BDR subspace clustering algorithms, the proposed method improves the clustering accuracy by 11% and 8% on Extended Yale B dataset, respectively. The proposed method can better fit the distribution of data singular values and construct the similarity matrices more accurately.

Foundation Support

国家自然科学基金资助项目(61801198)
江苏省自然科学基金资助项目(BK20180174)
江苏省青蓝工程资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.04.0219
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Algorithm Research & Explore
Pages: 72-75,158
Serial Number: 1001-3695(2024)01-011-0072-04

Publish History

[2023-07-25] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

刘明明, 羊远灿, 杨研博, 等. 面向矩阵秩函数准确估计的自表示子空间聚类方法 [J]. 计算机应用研究, 2024, 41 (1): 72-75,158. (Liu Mingming, Yang Yuancan, Yang Yanbo, et al. Low rank subspace clustering algorithm based on accurate estimation for matrix rank function [J]. Application Research of Computers, 2024, 41 (1): 72-75,158. )

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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