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