Individuals Matter: Improving Deep Multi-View Clustering via Explicit Single-View Enhancement

Although existing deep multi-view clustering methods often achieve high performance, they predominantly focus on improving the fused representation while overlooking the enhancement of individual views, often resulting in stagnant or inconsistently improved view-specific representations that limit the final fusion quality. To address this, we propose Explicit single-View Enhancement (EVE), a novel deep multi-view clustering framework designed to explicitly optimize the clustering capability of each individual view. Specifically, EVE employs an attention-based module to generate a global consensus as a reliable anchor, which subsequently guides the refinement of individual views through two key components: feature-level selective alignment and structural-level neighborhood propagation. Crucially, the enhanced single views will further boost the fused representation, and finally leading to a mutually reinforcing closed-loop feedback process. Our method is also naturally applicable to incomplete deep multi-view clustering scenarios. Extensive experiments on multiple benchmark datasets demonstrate that EVE consistently outperforms state-of-the-art competitors, achieving substantial average improvements of 5.9% in ARI across all datasets.The code is available in the Supplementary Material.