Algorithm Research & Explore
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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. )

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  • Application Research of Computers Monthly Journal
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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.

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