Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing one true label and several false positives. Existing MIPL algorithms have primarily focused on mapping multi-instance bags to candidate label sets for disambiguation, disregarding the intrinsic properties of the label space and the supervised information provided by non-candidate label sets. In this paper, we propose an algorithm named ELIMIPL, i.e., Exploiting conjugate Label Information for Multi-Instance Partial-Label learning, which exploits the conjugate label information to improve the disambiguation performance. To achieve this, we extract the label information embedded in both candidate and non-candidate label sets, incorporating the intrinsic properties of the label space. Experimental results obtained from benchmark and real-world datasets demonstrate the superiority of the proposed ELIMIPL over existing MIPL algorithms and other well-established partial-label learning algorithms.

翻译：多示例偏标记学习（MIPL）旨在处理每个训练样本表示为一个多示例包，并关联一个包含一个真实标签和若干假阳性标签的候选标签集的场景。现有MIPL算法主要侧重于将多示例包映射到候选标签集以进行消歧，忽略了标签空间的内在特性以及非候选标签集提供的监督信息。本文提出一种名为ELIMIPL（即利用共轭标签信息的多示例偏标记学习）的算法，该算法利用共轭标签信息来提升消歧性能。为实现这一目标，我们同时提取嵌入在候选标签集和非候选标签集中的标签信息，并融入标签空间的内在特性。在基准数据集和真实数据集上获得的实验结果表明，所提出的ELIMIPL算法优于现有的MIPL算法及其他成熟的偏标记学习算法。