Semantic Calibration Beyond Anchor Correspondence for Multi-View Clustering

Multi-view clustering aims to uncover a shared partition from heterogeneous representations of the same underlying data. Most existing methods emphasize cross-view consistency, implicitly assuming that a global category exhibits compatible local semantics across views. In practice, however, differences in modalities, feature representations, anchor construction, missing or degraded observations, and other view-specific factors can induce class-level semantic shifts, making this assumption overly restrictive. We propose Calibrated Anchor Semantic Transitions (CAST), a probabilistic framework that explicitly models how global cluster semantics are expressed through view-local semantic states. CAST operates directly on heterogeneous sample-to-anchor assignments, allows different anchor cardinalities across views, and avoids requiring cross-view anchor correspondence. Learned class-level transitions use cross-view evidence to infer global semantics while identifying and calibrating systematic sample–view shifts. We further establish theoretical properties concerning correspondence-free semantic evidence, semantic discriminability, multi-view complementarity, and the convergence behavior of the proposed MAP-EM optimization. Extensive experiments on multiple public multi-view clustering benchmarks, together with controlled semantic-shift evaluations, demonstrate strong clustering performance across both naturally heterogeneous and deliberately shifted settings. Ablation studies on the semantic transition structure further verify that explicitly modeling class-level semantic transitions is important for capturing cross-view semantic heterogeneity.