Incomplete multi–view partial multi–label learning network with structure–aware consistent fusion

In recent years, incomplete multi-view partial multi-label classification has attracted growing attention due to its practical relevance. However, many existing methods rely on equal-weight (average) fusion and thus overlook sample-wise reliability differences across views, making the fused representations vulnerable to noise and missing views. To address this issue, we propose the Structure-Aware Consistency Fusion Network (SACF-Net) for incomplete multi-view partial multi-label classification.