Multi-Granularity Relation Transfer with Shared Anchors for Incomplete Multi-View Clustering

Multi-granularity modeling aims to capture complementary cluster structures from fine to coarse resolutions to enhance incomplete multi-view clustering. Unfortunately, view incompleteness disrupts such modeling by inducing two major problems: cross-view reference inconsistency and cross-granularity structural instability. The first is reference inconsistency, where missing views make relational structures depend on different observed sample subsets, making structures from different views and granularities difficult to integrate. Considering this, we introduce shared anchors as a unified relational reference and construct masked sample-anchor assignments, which are further aggregated into a sample-complete global reference without imputing missing views. The second is multi-granularity instability: fine-grained structures are sensitive to missingness and noise, while coarse-grained structures may suffer from semantic drift. Therefore, we develop an anchor-induced multi-granularity relation transfer mechanism to extract fine-to-coarse structures on compact anchor graphs and transfer them to samples. By integrating sparse local geometry, our model preserves supported neighborhood evidence and promotes local consistency. Additionally, adaptive low-rank consensus learning adjusts the relative view-granularity contributions according to their consistency with the evolving consensus. Extensive experiments conducted on multiple benchmark incomplete multi-view datasets demonstrate the superiority of the proposed method over existing state-of-the-art approaches.