Coupling Error Exploration for Incomplete Multi-View Missing Multi-Label Classification

Incomplete Multi-view Missing Multi-label Classification (IM3C) aims to predict potentially relevant labels for each sample by exploiting the consensus and complementary information across available views and labels. Although existing IM3C methods have made notable progress, the missingness of some views makes it difficult for view recovery or feature fusion to fully preserve the view-specific distributional structure, leading to the Representation Bias Problem (RBP). Furthermore, the RBP reduces the reliability of label predictions, while incomplete labels provide insufficient or even erroneous supervision, thereby interfering with representation learning and continuously amplifying prediction errors, i.e., Coupling Error Propagation (CEP). To address these challenges, we propose Coupling Error Exploration (CEE) for incomplete multi-view missing multi-label classification, aiming to achieve robust and accurate label prediction by exploiting sample-level and label-level structural information. Specifically, to alleviate RBP, we first recover the missing views and ensure the view-specific distribution structure through multi-view rectified flow learning. Furthermore, we propose multi-view structure reinforcement that utilizes multi-view contrastive learning to improve semantic consistency across views. Finally, to mitigate CEP, we design collaborative dual-graph structure learning, which enhances sample representations by leveraging sample graph relationships and explores label semantic correlations through label graph relationships. Through dual-graph collaboration, we can uncover comprehensive relational information both between samples and between views. Theoretical analyses and extensive experimental results demonstrate that CEE outperforms state-of-the-art methods.