Theory-Driven Label-Specific Representation for Incomplete Multi-View Multi-Label Learning
Quanjiang Li, Tianxiang Xu, Tingjin Luo, Yan Zhong, Yang Li, Yiyun Zhou, Chenping Hou
Multi-view multi-label learning typically suffers from dual data incompleteness due to limitations in feature storage and annotation costs.The interplay of heterogeneous features, numerous labels, and missing information significantly degrades model performance. To tackle the complex yet highly practical challenges,  we propose a Theory-Driven Label-Specific Representation (TDLSR) framework. Through constructing the view-specific sample  topology and prototype association graph,  we develop proximity-aware imputation via attention-based relational propagation, while deriving class representatives that capture the label correlation semantic. To obtain semantically distinct view representations,  we introduce  principles of information shift,  interaction and orthogonality, which promotes task-relevant feature disentanglement and mitigates message distortion and redundancy.  Building upon the mutual information optimization model, we formulate its theoretical upper bound into its objective. Moreover, label-semantic-guided feature learning is employed to identify the  discriminative shared and specific representations and  refine the label preference across views. Finally, extensive experiments on public datasets and real-world applications validate the effectiveness of TDLSR.