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

Multi-view Multi-label Learning (MVML) aims to leverage multi-view information from input samples to achieve accurate predictions of multiple labels. Unfortunately, most existing MVML methods operate under the assumption of data completeness, which makes them ineffective in practical scenarios involving missing views or uncertain labels. Recent methods address incomplete data, but few approaches handle scenarios where both views and labels are missing. To address this challenge, we propose a 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 that features within the same view maintain high semantic relevance under the condition of view missing, while the semantics between different views are different. Unlike previous methods, DFFL assumes that label relevance can be reversely mapped to high-dimensional features. By establishing View-consistency learning, the 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 through dual prediction, thereby deriving the maximum joint distribution of feature fusion and combining the missing view index matrix to achieve feature fusion. This process can effectively alleviate the fusion feature suppression existing in previous methods. Finally, the missing label index matrix is combined with the fusion feature to complete the classification task. We validate the framework on five widely used datasets, and experimental results demonstrate that our approach achieves superior performance compared to state-of-the-art methods. Ablation studies further validated the effectiveness of each component in DFFL.