Tri-perspective Multi-view Classification

Multi-view classification aims to improve prediction performance by integrating heterogeneous data sources, leveraging the complementary and consistent information across different views. However, existing approaches predominantly focus on consistency and complementarity, often overlooking the role of diversity, which is crucial for enhancing generalization and mitigating redundancy. To address these issues, we propose a Tri-perspective Multi-view Fusion and Classification (TMFC) framework that systematically unifies consistency, complementarity, and diversity principles. TMFC consolidates multi-view data into a meta-view through feature concatenation and generates diverse latent views via random mapping, preserving structural information while reducing redundancy. These latent views are optimized through a unified formulation that balances alignment, information enrichment, and feature distinctiveness, reformulated as a semi-definite programming problem and efficiently solved using the reformulation-linearization technique with a cutting plane algorithm. Extensive experiments on real-world datasets demonstrate TMFC’s superiority over state-of-the-art methods, achieving significant improvements in accuracy, normalized mutual information, and adjusted rand index.