Partially View-Aligned Representation Learning via Cross-View Graph Contrastive Network

Abstract

Multi-view representation learning, aimed at uncovering the inherent structure within multi-view data, has developed rapidly in recent years. In practice, due to temporal and spatial desynchronization, it is common that only part of the data is aligned between views, which leads to the Partial View Alignment (PVA) problem. To address the challenge of representation learning on partially view-aligned multi-view data, we propose a new cross-view graph contrastive learning network, which integrates multi-view information to align data and learn latent representations. First, view-specific autoencoders are used to construct an end-to-end multi-view representation learning framework for learning specific view representations. Furthermore, to achieve cluster-level alignment, we introduce a cross-view graph contrastive learning module to guide the learning of discriminative representations. Compared to the existing methods, the proposed cluster-level alignment method successfully extends the view alignment to more than two views. Meanwhile, the results of clustering and classification experiments on several popular multi-view datasets can also illustrate the effectiveness and superiority of the proposed method.