Dual Feature Fusion for Incomplete Multi-View Multi-Label Learning

Multi-view Multi-label Learning (MVML) leverages multi-view information to accurately predict multiple labels. Unfortunately, most existing MVML methods assume data completeness, making them ineffective in scenarios involving missing views or uncertain labels. Recent methods address incomplete data, yet few handle simultaneous view and label absence. To address this, we propose the Dual-view Feature-guided Fusion Learning (DFFL) framework. DFFL considers both view-specific unique features and inter-view consistent features. Specifically, DFFL constructs view uniqueness contrastive learning to ensure features within the same view maintain high semantic relevance despite missing views, while distinguishing inter-view semantics. Unlike previous methods, DFFL assumes label relevance can be reversely mapped to high-dimensional features. By establishing View-consistency learning, mutual information in the shared embedding space is maximized to achieve consistent feature alignment. In particular, DFFL minimizes the conditional entropy of the marginal distribution of multi-view features via dual prediction, deriving the maximum joint distribution for feature fusion combined with the missing view index matrix. This process effectively alleviates fusion feature suppression. Finally, the missing label index matrix is combined with fusion features to complete classification. We validate the framework on five datasets, where results demonstrate superior performance compared to state-of-the-art methods. Ablation studies further validate the effectiveness of each component.