Contrastive and View-Interaction Structure Learning for Multi-view Clustering

Existing Deep Multi-view Clustering (DMVC) approaches typically concentrate on capturing consensus semantics from multiple views, where contrastive learning is widely used to align view-specific representations of each view. Unfortunately, view-specific representations are extracted from the content information of the corresponding instance, neglecting the relationships among different instances. Furthermore, existing contrastive loss imports numerous false negative pairs that conflict with the clustering objectives. In response to these challenges, we propose a contraStive and viEw-interaction stRucture learning framework for multI-viEw cluStering (SERIES). Our method takes into account the structural relations among instances and boosts the contrastive loss to improve intra-class compactness. Meanwhile, a cross-view dual relation generation mechanism is introduced to achieve the consensus structural graph across multiple views for clustering. Specifically, we initially acquire view-specific representations using multiple graph autoencoders to exploit both content information and structural information. Furthermore, to pull together the same cluster instances, a soft negative pair aware contrastive loss is employed to distinguish the dissimilar instances while attracting similar instances. Thereafter, the view-specific representations are fed into cross-view dual relation generation layers to generate the affinity matrices of each other, aiming to reveal a consistent structural graph across various views. Extensive experiments conducted on six benchmarks illustrate the superiority of our method compared to other state-of-the-art approaches.