Multi-view learning with graph convolution networks adopting diverse graphs and genuine deep feature fusion

Abstract

Multi-view data significantly enhances the accuracy of machine learning algorithms by providing a comprehensive representation of object features. Despite their potential, research on the use of Graph Convolutional Networks (GCNs) for processing node connectivity and data features remains limited. Existing methods mainly focus on weighted summation of graph matrices, with only a few approaches effectively integrating the feature information into the graph structures. To overcome these limitations, this paper proposes a novel deep learning architecture: the Feature Fusion and Multi-Graph Fusion Learning Framework (MGCN-FN). The framework consists of two core modules: Feature Fusion Network (FFN): Designed to extract and consolidate key features from multiple views. Multi-Graph Fusion Network (MGFN): Constructs multiple graphs for each view and jointly optimizes both the graph weights and the GCN model. Extensive experiments on various multi-view datasets show that MGCN-FN achieves superior performance compared to state-of-the-art methods, especially on semi-supervised multi-view classification tasks.