Quality-to-Relation Learning for Multi-View Clustering under Heterogeneous Noise

Quality-aware multi-view clustering (QAMVC) has recently been explored to address heterogeneous observation reliability by estimating data quality and using it to regulate representation learning and multi-view fusion. However, existing QAMVC methods often provide coarse-grained quality estimates based on limited quality cues, and mainly use them to adjust feature contributions or fusion weights, leaving the relational influence of unreliable observations insufficiently regulated. Moreover, weak relations may be suppressed or discarded even when they receive partial support from other views. To address these issues, we propose Cross-Granularity Quality-to-Relation Learning (CGQRL), a unified framework that establishes a path from sample-view quality to relational supervision. CGQRL first estimates dynamic sample-view quality by jointly considering reconstruction fidelity, cross-view consistency, and cluster compatibility. The resulting quality scores are used to adaptively weight view-specific representations for consensus fusion and, more importantly, transferred to the relational level to characterize the reliability of sample neighborhood relations and cross-view assignment relations. Based on these quality-aware relations, we further develop a reliability-preserving structural alignment strategy that provides soft structural supervision: reliable relations receive stronger supervision, partially supported relations are down-weighted rather than discarded, and relations unsupported across views are treated as explicit negatives. By jointly modeling observation quality and relational influence, CGQRL suppresses unreliable structural information while preserving potentially useful weak relations. Extensive experiments on multiple multi-view clustering benchmarks demonstrate the effectiveness of CGQRL under heterogeneous noise.