Conquering Missing Views: Scalable Incomplete Multi-View Clustering with Anchor Graph Propagation

Recently, incomplete multi-view clustering has emerged as a powerful solution for discovering consistent cluster structures despite the presence of missing views. When handling incomplete data, existing methods usually reconstruct missing features from observed views or estimate similarities from known inter-sample relationships, yet they often fail to balance clustering performance with computational efficiency. Although anchor graph learning has shown strong effectiveness in multi-view clustering, its extension to incomplete scenarios remains a non-trivial challenge. Furthermore, the reliance on iterative optimization involving coupled variables significantly hinders the scalability of existing models. In response to these challenges, we proposeSSR-a method designed forscaling,simplifying, androbustifying multi-view clustering. SSR introduces a scalable framework empowered by a novel anchor selection strategy tailored for incomplete multi-view data. In this framework, graph propagation is employed to impute missing information, enabling effective information diffusion across views. In contrast to traditional methods that rely on computationally intensive iterative optimization, SSR adopts a forward-only, low-complexity design, significantly enhancing scalability. Moreover, isolation masks are incorporated into the graph propagation process to improve robustness. Extensive experiments on multiple datasets demonstrate the superior clustering accuracy and computational efficiency of SSR, establishing it as a practical and efficient solution for multimedia analysis in real-world scenarios.