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. 40, 2023 No. 1
155-159

Graph convolutional networks based on structural errors

Wu Lin
Xu Ruyu
Su Xingwang
Huang Jinbo
Wang Xiaoming
School of Computer & Software Engineering, Xihua University, Chengdu 610039, China
DOI: 10.19734/j.issn.1001-3695.2022.05.0279

Abstract

In view of the problems that select cross entropy as a loss function in a graph convolution network may lead to the over-training and the weak generalization ability of the model in a small sample data sets, this paper proposed a graph convolution network based on structural error. Using the improved support vector machine(SVM) as the classifier of the graph convolution network could reduce the risk of over-fitting of the model. Based on the generalization error theory of SVM, improving the loss function of SVM, the proposed method maximized the interval of different samples and limited the interval of similar samples, improved the generalization ability of the model. Firstly, it calculated the average distance from the feature vector to the center point in the feature space, used it to approximately replace the radius of the sphere, and then the new loss function would guide model learning. Experiments on the NTU RGB+D60 and NTU RGB+D120 datasets in the field of behavior recognition based on skeleton prove that compared with the traditional graph convolution network model, the proposed method can obviously improve the recognition accuracy and has better generalization performance.

Foundation Support

四川省自然科学基金资助项目(2022NSFSC0533)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.05.0279
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 1
Section: Algorithm Research & Explore
Pages: 155-159
Serial Number: 1001-3695(2023)01-025-0155-05

Publish History

[2022-08-26] Accepted Paper
[2023-01-05] Printed Article

Cite This Article

吴琳, 许茹玉, 粟兴旺, 等. 基于结构误差的图卷积网络 [J]. 计算机应用研究, 2023, 40 (1): 155-159. (Wu Lin, Xu Ruyu, Su Xingwang, et al. Graph convolutional networks based on structural errors [J]. Application Research of Computers, 2023, 40 (1): 155-159. )

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)