Recoupling Multi-Tensorized Boost Consistency Graph Learning for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) has grasped growing attention recently, which aims to address unsupervised clustering problems caused by missing views. While several method has been proposed, the current approaches still suffer from key shortcomings: Affinity graph learning methods based on Euclidean distances can be sensitive to feature magnitudes, leading to inadequately reflect informative relative response patterns. Moreover, existing methods recover missing view-specific information without considering cross-view diversity, resulting in suboptimal imputation. To overcome these challenges, we introduce a novel framework termed recoupling multi-tensorized boost consistency graph learning (RMTC-GL) for IMVC. Our approach first constructs correlation graphs using non-parametric Spearman correlation on complete samples, then recovers missing entries by preserving and leveraging all observed correlations via a masking strategy. To maintain view diversity, we incorporate a tensor-based diversity constraint that explicitly enforces heterogeneity among the recovered graphs across views. Additionally, we design a recomposed multi-tensor enhanced consensus learning module that jointly exploits cross-view complementarity for direct consensus graph recovery and captures high-order consistency between complete and incomplete correlation structures. Extensive experiments demonstrate that RMTC-GL achieves superior clustering accuracy compared to state-of-the-art methods.