Partial Multi-label Learning with Label Embedding and Dynamic Noise Thresholding

Partial Multi-label Learning (PML) is a typical weakly supervised learning paradigm. In the complex label noise environment, PML models need semantic correlation features to build an adaptive perception for heterogeneous noise levels of labels. Nevertheless, most existing mainstream PML methods adopt a fixed-threshold strategy for label information propagation, and this strategy exacerbates the bottleneck of label ambiguity, making it unable to adapt to differentiated sample noise scenarios. To address the above situation, this paper proposes a novel label correlation-driven partial multi-label learning algorithm with dynamic noise threshold learning (PML-LE). The PML-LE realizes the embedding of label semantic features and correlation features through the label correlation modeling module to provide semantic support for the subsequent label disambiguation process. Meanwhile, PML-LE designs an adaptive noise thresholding mechanism based on the information entropy of candidate label sets, which dynamically adjusts the pruning strictness of label adjacency matrices according to each sample’s noise level. Experimental results on benchmark datasets demonstrate that the proposed PML-LE algorithm achieves state-of-the-art performance for PML.