In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 11
3343-3349

Structured graph embedding for unsupervised feature extraction

Yuan Fengyan1,2
Yin Xuesong2
Wang Yigang2
1. Pinghu School, Zhejiang Open University, Pinghu Zhejiang 314200, China
2. Dept. of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China
DOI: 10.19734/j.issn.1001-3695.2024.03.0072

Abstract

Feature extraction is one of the most effective tools for processing high-dimensional data. However, existing feature extraction methods suffer from two problems: they do not capture both the local and global structures of the data simultaneously, the constructed graph is disconnected from the number of data cluster and does not have an exact connected component. To address these issues, this paper proposed a SGE for unsupervised feature extraction. By constructing K-nearest neighbor graph for data representation and using least squares regression, SGE can simultaneously respect the local and global correlation structures of the data. Moreover, by enforcing rank constraints on the Laplacian matrix of the representation, SGE constructed the optimal graph of c connected components with c clusters, and thus revealed the clustering structure of the data. Therefore, the proposed SGE can find more discriminative projections. Experiments on real-world datasets show that SGE outperforms other mainstream dimensionality reduction methods. Especially on the PIE dataset, the clustering accuracy of SGE is 18.7% higher than that of LRPP_GRR. These results indicate that the proposed SGE algorithm can effectively reduce the dimensionality of the data.

Foundation Support

浙江省高等学校国内访问学者资助项目(FX2023191)
浙江开放大学312人才培养工程资助项目
浙江省公益技术应用研究项目(LGG22F020032)
温州市基础性公益科研项目(G2023093)
浙江省重点研发计划重点专项资助项目(2021C03137)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.03.0072
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 11
Section: Algorithm Research & Explore
Pages: 3343-3349
Serial Number: 1001-3695(2024)11-020-3343-07

Publish History

[2024-05-20] Accepted Paper
[2024-11-05] Printed Article

Cite This Article

袁凤燕, 尹学松, 王毅刚. 面向无监督特征提取的结构化图嵌入 [J]. 计算机应用研究, 2024, 41 (11): 3343-3349. (Yuan Fengyan, Yin Xuesong, Wang Yigang. Structured graph embedding for unsupervised feature extraction [J]. Application Research of Computers, 2024, 41 (11): 3343-3349. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)