Complete-Sample-Driven Shared Semantic Prototype Learning for Highly Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to discover latent cluster structures from multi-source data with missing views. However, under high missing rates, the limited cross-view correspondences often lead to unstable representations, semantic misalignment across views, and biased cluster prototypes. To address these issues, we propose a complete-sample-driven framework with shared semantic prototypes, denoted as CSSP, for highly incomplete multi-view clustering. Specifically, a small set of complete samples is first used to pretrain view-specific autoencoders for stable representation initialization. During prototype training, artificial missing-view tasks are constructed by randomly masking complete samples, and batch-wise missing-view consistency regularization is imposed on latent representations, reconstructions, and prototype assignments, enhancing the model’s adaptability to diverse missing patterns. Furthermore, shared semantic prototypes are constructed from complete samples to provide a reliable global semantic reference across views. Shared assignment consistency and prototype-level soft bridging are then introduced to align view-specific prototypes with the shared semantic space. Finally, the learned view prototypes are used to recover missing latent representations for clustering. The proposed method effectively exploits reliable cross-view semantics from a limited number of complete samples without relying on complex generative view recovery. Extensive experiments on multiple multi-view datasets under various high-missing-rate settings demonstrate competitive clustering performance and strong robustness, particularly in highly incomplete scenarios.