Graph Convolutional Network With Self-Augmented Weights for Semi-Supervised Multi-View Learning

Recently, owing to the effectiveness in exploiting inherent connections between data in different views, graph-based deep learning approaches have gained widespread popularity in semi-supervised multi-view tasks. Generally, the existing approaches fuse the information from different views via the linear or nonlinear weight strategies, which distinguish the importance of different views by attributing their weights between [0, 1] , i.e., some less important views are discarded since assigned with 0 and the pivotal views are not enhanced. However, these view-weighting strategies ignore the complementary information from the less important views. To address this issue, a superior-performing graph convolutional network (GCN) with self-augmented weights is proposed. The proposed self-augmented weight strategy is based on exponential series integration, which preserves the less important views and simultaneously strengthens the key views for multi-view fusion. Specifically, the designed weight strategy can adaptively preserve the complementary information from the less important views by assigning nonzero weights and strengthen the pivotal views by assigning higher weights based on exponential series integration. Besides, to further improve the model performance, an orthogonal constraint layer with a forced orthogonal weight is introduced, which is capable of making the representation more discriminative. Extensive experiments demonstrate the superiority of the proposed method.