Deep multi-view clustering based on instance-level adaptive structural contrastive learning

Deep multi-view clustering has rapidly developed in recent years, leveraging the powerful representation capabilities of deep neural networks. Among them, instance-level feature contrastive learning is widely used in deep multi-view clustering to align the embedded features of the same samples across views. However, it overlooks the constraint on the clustering structure consistency across views. Drawing inspiration from the instance-level feature contrastive learning mentioned above, we propose deep multi-view clustering based on instance-level adaptive structural contrastive learning. First, considering that different views may have varying impacts on the clustering results of different multi-view samples, we utilize Transformer Pooling module to adaptively fuse different views of different samples, obtaining the fused view. The final clustering results are derived from the fused view as well. Secondly, we propose a clue-consistency-based approach to identify sample pairs that exhibit consistent clustering structures between each view and the fused view, forming local and global consistency clustering information. Incorporating global consistency clustering information, we construct adjacency matrices for each view and the fused view. Since adjacency matrices record the clustering structure of each sample with others, we propose the instance-level adaptive structural contrastive learning, leveraging the above local consistency information to align the overall clustering structures of the same samples across different views and the fused view. By comparing the results of proposed method with several state-of-the-art methods on multiple multi-view datasets, we demonstrate the superiority of the proposed approach.