Self-adaptive label discovery and multi-view fusion for complementary label learning.

Unlike traditional supervised classification, complementary label learning (CLL) operates under a weak supervision framework, where each sample is annotated by excluding several incorrect labels, known as complementary labels (CLs). Despite reducing the labeling burden, CLL always suffers a decline in performance due to the weakened supervised information. To overcome such limitations, in this study, a multi-view fusion and self-adaptive label discovery based CLL method (MVSLDCLL) is proposed. The self-adaptive label discovery strategy leverages graph-based semi-supervised learning to capture the label distribution of each training sample as a convex combination of all its potential labels. The multi-view fusion module is designed to adapt to various views of feature representations. In specific, it minimizes the discrepancies of label projections between pairwise views, aligning with the consensus principle. Additionally, a straightforward mechanism inspired by a teamwork analogy is proposed to incorporate view-discrepancy for each sample. Experimental results demonstrate that MVSLDCLL learns more discriminative label distribution and achieves significantly higher accuracies compared to state-of-the-art CLL methods. Ablation study has also been performed to validate the effectiveness of both the self-adaptive label discovery strategy and the multi-view fusion module.