From Missing Relations to Reliable Structure: Cross-View Relation Recovery and Latent Structure Estimation for Incomplete Multi-View Learning

In real-world applications, multi-view data are often incomplete because some views are unavailable for some samples. Although existing incomplete multi-view learning methods have made substantial progress in feature recovery and structural modeling, view missingness can also cause relational information to disappear when relation endpoints are unavailable, leaving the underlying structure incomplete. To address this issue, we propose Reliable Latent Structure Estimation (ReLaS), a relation-centric framework that explicitly restores missing relational structure and learns a latent consensus graph from incomplete multi-view observations. For relations that remain directly observable, ReLaS estimates their relation evidence from endpoint availability and cross-view edge confirmations, while characterizing their reliability. Relations rendered unobservable by missing endpoints are explicitly recovered through cross-view structural correspondence, with the reliability of recovered relations further calibrated in a self-supervised manner. Both direct and recovered relation evidence are then integrated under relation-profile consistency to obtain the latent consensus graph. Experiments on multiple benchmark datasets under different view-missing rates demonstrate the effectiveness of ReLaS in restoring relational structure and improving semi-supervised classification performance.