Multi-faceted Complementary Learning for Incomplete Multi-view Multi-label Classification

Due to data collection limitations and annotation reliability, the lack of multi-view data will weaken the comprehensive understanding of samples, and incomplete multi-view multi-label classification faces severe challenges. To address this problem, we propose a multi-view complementary learning framework MC-IVLC to explore the complementary information between views fully. Specifically, MC-IVLC proposes compensating for the collapse of reconstructed features and explicitly using fused features as supervisory signals to guide the completion of missing views. In addition, MC-IVLC fully utilizes the complementary relationship between views from both instance and semantic levels. Instance-level contrastive learning aims to promote the clustering of similar features in the same view to enhance the complementarity of cross-view features. Semantic-level contrastive learning utilizes pseudo-labels to infer missing labels in label embeddings. It combines pseudo-label semantic information with feature embeddings to guide the semantic relevance of cross-view features. Finally, MC-IVLC explicitly encodes view identity and introduces a view-label prediction loss term to enhance the perception of view information and align single views and multiple views, further exploring the intrinsic connection between views and labels. We conduct experiments on five widely used datasets. Experimental results show that MC-IVLC achieves excellent performance compared with state-of-the-art methods. Ablation studies further validate the effectiveness of each component.