Graph Convolutional Neural Networks with Geometric and Discrimination information. (September 2021)

• Record Type: Journal Article
• Authors: Shang, Ronghua; Meng, Yang; Zhang, Weitong; Shang, Fanhua; Jiao, Licheng; Yang, Shuyuan
• Is Part Of: Engineering applications of artificial intelligence. Volume 104(2021)
• Publication Date: 2021-09
• Subjects: Deep learning -- Convolutional neural network -- Spectral theory -- Local structure -- Discriminant information; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104364
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: In recent years, geometric deep learning methods have been proposed, which are called Graph Convolutional Neural Networks (GCNNs). GCNNs not only can extract effective features like the classical CNN, but also can effectively reflect the true geometric structure of original data. Although GCNNs consider the geometric structure of original data, they construct the same feature graph to perform graph convolution, and ignore the difference between the local structures of different samples. Therefore, a novel Graph Convolutional Neural Network with Geometric and Discrimination information (GDGCNN) is proposed, which integrates traditional machine learning ideas to further improve the performance of feature extraction. In order to exploit differences between the local structures of different samples and make full use of the geometric structure of original data, GDGCNN constructs different feature graphs for different training batches to fully exploit the local geometry of data. Moreover, the discriminant regularization is introduced into GDGCNN to effectively utilize the discriminant information contained in original data. Therefore, GDGCNN has good discriminative ability and robustness. The experimental results show that GDGCNN can perform feature extraction tasks very well, and it is superior to some existing methods for classification in terms of accuracy and F1-Score. Highlights: GDGCNN constructs different feature graphs for different training batches to address the issue in GCNNs of neglecting the difference between the local structures of different samples in the dataset. Therefore, it can fully exploit the local geometry of original data. GDGCNN effectively uses the discrimination information of original data to extract more discriminative features, and thus it has better learning ability and discriminating ability. GDGCNN integrates traditional machine learning ideas into the new framework of GCNN to further improve the performance of feature extraction.