Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data
This addresses a practical challenge in multi-view learning for applications with incomplete or misaligned data, though it is incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of representation learning on partially aligned multi-view data, where missing or unaligned views degrade performance, by proposing a cross-view graph contrastive learning framework that integrates multi-view information to align data and learn latent representations, with experiments on real datasets showing effectiveness in clustering and classification tasks.
Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete and aligned. This leads to an inevitable deterioration in their performance when encountering practical problems such as missing or unaligned views. To address the challenge of representation learning on partially aligned multi-view data, we propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Compared with current approaches, the proposed method has the following merits: (1) our model is an end-to-end framework that simultaneously performs view-specific representation learning via view-specific autoencoders and cluster-level data aligning by combining multi-view information with the cross-view graph contrastive learning; (2) it is easy to apply our model to explore information from three or more modalities/sources as the cross-view graph contrastive learning is devised. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.