Confidence-Weighted Dual-Expert Correspondence Refinement for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to partition all samples effec-tively when each sample is observed in only a subset of views. However, mostexisting IMVC methods commonly treat the observed cross-view correspon-dences as equally reliable supervision, while overlooking the varying semanticand structural consistency of these correspondences under incomplete and het-erogeneous observations. To address this limitation, we propose an anchoreddual-expert framework that progressively learns confidence-weighted cross-viewcorrespondences from complementary first- and second-order structural informa-tion. Specifically, we first exploit the observed partial overlap to initialize softcross-view correspondences and select an informative anchor view, providing aninitial basis for correspondence refinement. Furthermore, a dual-expert matchingmechanism is developed, which combines a Koopmans–Beckmann QAP expertfor modeling first-order node and local relational affinities with an anchor-basedLawler QAP expert for capturing higher-order structural consistency in a compactlearned anchor space. The latter incorporates an outlier state to accommodate un-matched structures caused by incomplete observations. Finally, we introduce amomentum-based consistency refinement mechanism to fuse the complementaryevidence from the two experts and iteratively update correspondence confidences,which in turn provide progressively refined supervision for multi-view representa-tion learning. The refined cross-view and within-view affinities are subsequentlyintegrated to construct a global affinity matrix for spectral clustering. Extensiveexperiments under varying view missing rates demonstrate the effectiveness androbustness of the proposed framework.