Prototype-Routed Flow Matching for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to learn cluster-discriminative representations from partially observed views. Completion-based methods estimate missing-view representations so that observed and completed views can be used together for clustering. However, direct prediction and prototype-based reconstruction typically produce a single estimate for each missing view. When the observed views cannot uniquely determine the missing view, a single estimate may weaken cluster separation. Generative completion can instead model a conditional distribution over missing-view representations, but iterative denoising makes diffusion-based completion costly during training. We propose Prototype-Routed Flow Matching (PRFM), which formulates missing-view completion as clustering-aligned conditional transport in a shared latent space. PRFM uses dynamically updated online prototypes as routing signals during flow matching, transforming missing-view completion from static point prediction into a prototype-routed transport process. At each generation step, the assigned prototype provides a cluster-level direction, while a residual velocity captures sample- and view-specific variation. The same prototypes also prioritize observable candidates for cluster-consistent soft supervision and optimize completed representations in the prototype space. Completed latents are produced through few-step ODE inference. Experiments on multiple IMVC benchmarks show that PRFM achieves competitive clustering performance, remains robust under high missing rates, and offers favorable training and inference efficiency compared with diffusion-based completion. The code will be released upon acceptance.