Correlative Fusion-Based Multi-Instance Partial Multi-Label Learning

Multi-instance partial multi-label learning (MIPML) addresses a challenging scenario wherein each training sample comprises a bag of multiple instances associated with a candidate label set comprising several true labels alongside noisy labels simultaneously. Current MIPML methods typically neglect the essential correlations between labels and instances at both the instance and bag levels, which limits their effectiveness in disambiguation and predictive accuracy. To address these limitations, we present Correlation-Fusion MIPML (CF-MIPML), an innovative framework that integrates Label Confidence Generation (LCG) and Candidate Label Disambiguation (CLD). The LCG module systematically constructs a robust label confidence matrix by capturing correlation structures within and across bags, thereby providing a foundation for precise label disambiguation. The CLD module utilizes the comprehensive confidence matrix to further improve label predictions, utilizing an optimized iterative fusion loss function that incorporates partial loss and interaction loss. This joint-loss strategy enables ongoing refinement of label confidence during training, thereby improving the robustness and accuracy of predictions. Comprehensive experimental results on various benchmark and real-world datasets demonstrate that CF-MIPML outperforms existing state-of-the-art methods, enhancing handling of complex label ambiguity and improving overall model generalization in practical MIPML scenarios.