Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears all views, it is common real-world applications for instances to be missing from some resulting incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) learning, which aims use multiple views as fully possible flexible manner. By unified latent representation, trade-off between consistency and complementarity among different implicitly realized. explore the complex relationship samples representations, neighborhood constraint view-existence are proposed, first time, construct heterogeneous graph. Finally, avoid any inconsistencies training test phase, transductive technique applied based on graph classification tasks. Extensive experimental results datasets demonstrate effectiveness of our model over existing state-of-the-art approaches.