Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification

Multi-view data involves various data forms, such as multi-feature, multi-sequence and multimodal data, providing rich semantic information for downstream tasks. The inherent challenge of incomplete multi-view missing multi-label learning lies in how to effectively utilize limited supervision and insufficient data to learn discriminative representation. Starting from the sufficiency of multi-view shared information for downstream tasks, we argue that the existing contrastive learning paradigms on missing multi-view data show limited consistency representation learning ability, leading to the bottleneck in extracting multi-view shared information. In response, we propose to minimize task-independent redundant information by pursuing the maximization of cross-view mutual information. Additionally, to alleviate the hindrance caused by missing labels, we develop a dual-branch soft pseudo-label cross-imputation strategy to improve classification performance. Extensive experiments on multiple benchmarks validate our advantages and demonstrate strong compatibility with both missing and complete data.

Authors

• Jie Wen 0001
• Yadong Liu
• Zhanyan Tang
• Yuting He 0001
• Yulong Chen
• Mu Li 0005
• Chengliang Liu 0003

Keywords

• Multi-view multi-label classification
• Incomplete multi-view learning
• Missing multi-label classification