Robust and Dynamic Graph Convolutional Network For Multi-view Data Classification. (15th June 2021)

• Record Type: Journal Article
• Authors: Peng, Liang; Kong, Fei; Liu, Chongzhi; Kuang, Ping
• Is Part Of: Computer journal. Volume 64:Number 7(2021)
• Page Start: 1093
• Page End: 1103
• Publication Date: 2021-06-15
• Subjects: multi-view data -- graph convolutional network -- graph learning; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxab064
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Since graph learning could preserve the structure information of the samples to improve the learning ability, it has been widely applied in both shallow learning and deep learning. However, the current graph learning methods still suffer from the issues such as outlier influence and model robustness. In this paper, we propose a new dynamic graph neural network (DGCN) method to conduct semi-supervised classification on multi-view data by jointly conducting the graph learning and the classification task in a unified framework. Specifically, our method investigates three strategies to improve the quality of the graph before feeding it into the GCN model: (i) employing robust statistics to consider the sample importance for reducing the outlier influence, i.e. assigning every sample with soft weights so that the important samples are with large weights and outliers are with small or even zero weights; (ii) learning the common representation across all views to improve the quality of the graph for every view; and (iii) learning the complementary information from all initial graphs on multi-view data to further improve the learning of the graph for every view. As a result, each of the strategies could improve the robustness of the DGCN model. Moreover, they are complementary for reducing outlier influence from different aspects, i.e. the sample importance reduces the weights of the outliers, both the common representation and the complementary information improve the quality of the graph for every view. Experimental result on real data sets demonstrates the effectiveness of our method, compared to the comparison methods, in terms of multi-class classification performance.