Structure-guided decoupled contrastive framework for partial multi-view incomplete multi-label classification

Abstract

Abstract Recently, multi-view multi-label learning has gained significant attention due to its applicability in various domains. However, due to the limitations of data collection and the subjectivity of manual labeling, multi-view multi-label learning often faces both partial views and incomplete labels, substantially impacting the performance of existing classification methods in practical applications. Although existing methods have attempted to address this issue, they struggle to fully exploit the consistency and complementarity of multi-view multi-label data simultaneously. To overcome this limitation, we propose a novel structure-guided decoupled contrastive framework (SGDC). Specifically, to address the limitations of conventional single-encoder paradigms, SGDC innovatively incorporates a decoupled disentanglement mechanism (DDM). By integrating a dual-encoder architecture with mutual information upper bound constraints, DDM decouples multi-view features into view-specific and view-consistent components, which can improve the quality of feature representations. Additionally, the SGDC integrates a structure-guided contrastive (SGC) framework that performs dynamic semantic alignment in consistent feature spaces through global structural modeling, effectively implementing structure-conscious representation learning guided by the multi-view consensus principle. Experimental results on multiple datasets consistently demonstrate the effectiveness of our method in handling complex multi-view multi-label data.