TCRIMVC: Target-Conditioned Retrieval for Recovery and Refinement in Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to discover coherent cluster structures from multi-view data with partially missing observations, yet existing approaches often rely on dedicated cross-view predictors or generative models to infer unavailable representations, thereby introducing additional modeling assumptions and exposing downstream clustering to potential recovery errors. We revisit this problem from a retrieval perspective: relevant semantics may already exist among observed representations and can be reused as evidence for recovery. To this end, we propose a target-conditioned retrieval framework for incomplete multi-view clustering. Given the observed views of an instance and the identity of a requested view, TCRIMVC retrieves relevant latent evidence for representation construction. Missing-view representations are constructed by aggregating retrieved evidence, whereas observed representations are refined through a residual update that retains their original features. Both pathways retrieve from encoded observations, without reusing completed outputs as evidence. The resulting representations are jointly optimized through view reconstruction, global-to-view alignment, and confidence-aware balanced cross-view consistency, reducing the influence of uncertain recoveries on consensus learning. Extensive experiments on diverse benchmarks demonstrate the effectiveness and robustness of TCRIMVC under varying incomplete conditions. Further analyses validate the contributions of its retrieval, refinement, and consensus mechanisms, while cross-view image completion results illustrate the broader applicability of the proposed retrieval principle.