Label-Semantics-Guided Multi-View Multi-Label Learning via High-Order Semantic Fusion

Incomplete multi-view multi-label learning faces significant challenges arising from semantic heterogeneity across modalities and incomplete modality availability. Traditional fusion approaches typically emphasize superficial feature alignment, neglecting high-order semantic interactions among modalities and labels, thus resulting in redundant or conflicting information integration. To address these limitations, we propose a novel Label Semantic Guided Adaptive Fusion framework. Specifically, we leverage pretrained language models to generate semantic embeddings for both multi-view data and associated labels, facilitating unified semantic understanding. Subsequently, we construct dual-domain hypergraphs separately within the modality and label semantic spaces to explicitly model complex high-order semantic correlations. Based on these hypergraphs, we employ hypergraph neural networks to mine intrinsic semantic relationships and dynamically assess semantic consistency between each modality and the label space. Finally, an adaptive weighting strategy guided by this semantic consistency measure is introduced to fuse modalities effectively, assigning high weights to modalities with greater semantic alignment. Extensive experiments demonstrate that our LSGMM improves fusion accuracy and robustness over state-of-the-art IMvML methods, confirming the effectiveness of integrating label semantics and high-order semantic relationships into adaptive multi-view fusion.