LSPEL: Label-Specific Feature-Based Partial Label Learning for Emerging New Labels

In partial label learning(PLL) tasks, each training instance is assigned a candidate label set, with only one label being correct. Previous studies on PLL have focused on scenarios where the class label set remains fixed, i.e., the label set for test data is the same as that used during training. However, in many real-world applications, the environment is dynamic, and new labels may emerge, requiring methods that can detect and classify these new labels. Moreover, previous methods typically learn from partial label data by manipulating the same feature set, which may be suboptimal as it overlooks the semantic relationships between instances and labels. To this end, we develop a novel PLL approach called Label-Specific feature-based Partial label learning with Emerging new Labels(LSPEL), which works by iteratively learning label-specific features during the label disambiguation process to support new label detection and model update. It consists of three key components: 1) model training based on label-specific feature learning; 2) construction of a new label detector that works in conjunction with the classifier to predict known labels; and 3) model updating and induction to further enhance the prediction results for known labels. Extensive experiments on synthetic and real-world PL datasets demonstrate that LSPEL is effective in handling emerging new labels.