Adaptive multi-view consistency clustering via structure-enhanced contrastive learning

Current state-of-the-art deep multi-view clustering methods resort to contrastive learning to learn consensus representations with Cross-View Consistency (CVC). However, contrastive learning has inherent limitations when being applied to the multi-view clustering. On one hand, contrastive learning suffers from class collision issue, compromising the discriminability of consensus representation. On the other hand, contrastive alignment of two views of different quality could lead to representation degradation for the higher-quality view, weakening the robustness of the consensus representation. To alleviate these issues, this paper presents an Adaptive Multi-view consistency clustering method via structure-enhanced contrastive learning (AdaM), which learns multi-faceted consensus representation that balances view-consistency, discriminability and robustness, forming an optimal consensus representation. Specifically, we first design a view fusion module and a structural learning module to learn view weights and structural relationships among samples, respectively, to derive the consensus representation. Second, beyond CVC, we propose a novel clustering framework called Adaptive Multi-View Consistency (AMVC), which adaptively aligns specific view representation with consensus representation based on the learned view weights. Furthermore, compared to CVC, we theoretically demonstrate the superiority of AMVC in learning robust consensus representation. Third, AdaM leverages the structural relationships among samples to refine the conventional contrastive loss, further enhancing the discriminability of the consensus representation. Extensive experimental results on eight datasets demonstrate the superior performance of AdaM over eight advanced multi-view clustering baselines.