Cluster-aware contrastive learning for partially view-aligned clustering

Multi-view clustering aims to discover shared semantic information from different perspectives, but real-world time and space factors often lead to missing pairing relationships, resulting in partial data alignment between views and affecting clustering performance. Clustering on such data is referred to as the Partial View-aligned clustering Problem (PVP). Most existing PVP methods mainly capture shared semantic features in a common subspace to infer missing pairing relationships of samples between views. However, excessive reliance on shared feature representations between views could affect the enforcement of the clustering structures. To address this, we propose a novel Cluster-aware cOntrastive Learning method for partially view-Aligned clustering (COLA), by performing intra-view cluster contrastive learning to compact the clustering structure within a view and aligning the soft-label and distributions between views. In this way, the consistent clustering structure between views can be captured and then used to help recover the missing pairing relationships. Extensive experiments demonstrate that COLA outperforms state-of-the-art methods, improving NMI by 14.61% on average and improving ACC by 6.26% on average across eight datasets.