Sample-weighted fused graph-based semi-supervised learning on multi-view data

Research in semi-supervised learning on graphs has attracted more and more attention in recent years, as learning on graphs is applied in more and more domains and labeling data is expensive and time-consuming. Some scenarios have inherent graph structures in their data, such as the relationships between people in social scenarios or the relationships between objects that are mutually referenced. However, there are also many data types without inherent graph structures, such as image data, and each image can be described with different features, which is a typical type of multi-view data. For image data and other non-graph data, there are significantly fewer deep learning approaches that target multi-view graph-based semi-supervised learning. This paper attempts to fill this gap. Based on the Graph Convolutional Network (GCN) architecture, we propose a Sample-weighted Fused Graph-based Semi-supervised Classification (WFGSC) method for multi-view data in this paper. It (i) constructs a semi-supervised graph in each view using a flexible model for joint graph and label estimation, (ii) obtains an additional graph based on the representation of nodes provided by the joint estimator, and then obtains a fused graph between all views, (iii) gives higher weights to hard-to-classify samples, (iv) proposes a loss function to train the GCN on the fused features and the consensus graph that integrates graph auto-encoder loss and label smoothing over the consensus graph. The results of our experiments on six multi-view datasets show that our WFGSC performs well on both fused graph construction and semi-supervised classification, and generally outperforms traditional GCNs and other multi-view semi-supervised multi-view classification methods.1