Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels, among which only one is the ground-truth label. This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling. Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints, our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.

部分标签学习是一种弱监督学习框架，其中每个实例与多个候选标签相关联，其中只有一个是真实标签。本文提出了一个统一的公式，该公式为训练模型采用适当的标签约束，同时执行伪标签。与仅利用特征空间中的相似性而不利用标签约束的现有部分标签学习方法不同，我们的伪标记过程使用相同的候选标签约束来利用特征空间中的相似性和差异性，然后消除噪声标签的歧义。对人工和现实世界部分标签数据集的大量实验表明，我们的方法在分类预测方面明显优于最先进的同类方法。