Towards Discriminative Incomplete Multi-View Clustering via Prototype Diffusion

Incomplete multi-view clustering (IMVC) aims to learn unified representations from multi-source data with missing views to realize unsupervised clustering, which has broad application value in various real-world scenarios. Most existing IMVC methods only rely on limited paired complete samples to implement instance-level cross-view alignment and lack global semantic anchors. This drawback easily leads to cross-view semantic inconsistency and latent feature collapse, which further degrades the discriminability of representations. To tackle these issues, this paper proposes a Discriminative Prototype Diffusion framework named DPD. We adopt learnable shared prototypes as global semantic anchors to unify multi-view latent distributions through prototype-guided distribution alignment. Global category constraints are further introduced to mitigate latent feature collapse and restrain feature space compression. Meanwhile, the framework is equipped with mask-aware InfoNCE loss to achieve basic cross-view feature alignment by maximizing multi-view mutual information. We utilize the prototype alignment module to regularize latent diffusion inference for recovering missing views in the semantically aligned latent space. A confidence-aware prototype fusion module is constructed to adaptively fuse complementary information of all views and generate highly discriminative latent representations for clustering. Comparative experiments on multiple standard benchmark datasets demonstrate that the proposed DPD achieves superior clustering performance on incomplete multi-view clustering tasks.