  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.
