GRNet: Graph-based remodeling network for multi-view semi-supervised classification

Multi-view semi-supervised classification (MSSC) focuses on exploring information from multiple views of labeled and unlabeled data to boost classification performance. However, most of the existing methods build models for each view individually, therefore, the potential relationship between different views can’t be fully explored. Additionally, they either focus on the correlations for consistency or maximize the independence for complementarity. Consistency and complementarity are equally important for multi-view learning. Therefore, this work proposes a novel Graph-based Remodeling Network for MSSC (GRNet), which can explore the potential relationship of multiple views and balance the consistency and complementation adaptively. Specifically, this model integrates multiple views and then generates reformed pseudo views by an attention-based ensemble learning strategy. Moreover, to exploit the information of unlabeled data, this work introduces graph regularization for such a goal. Extensive experiments on several datasets demonstrate the effectiveness and efficiency of the proposed method.