Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

TL;DR

This paper introduces LHGN, a novel graph-based model that effectively handles incomplete multi-view data by learning a unified latent space, improving classification accuracy in real-world scenarios.

Contribution

The paper proposes a new Latent Heterogeneous Graph Network that models complex relationships in incomplete multi-view data using neighborhood and view-existence constraints.

Findings

Outperforms existing methods on real-world datasets

Effectively manages incomplete multi-view data

Utilizes a transductive learning approach for classification

Abstract

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.