As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true. Despite many methodology studies on learning from partial labels, there still lacks theoretical understandings of their risk consistent properties under relatively weak assumptions, especially on the link between theoretical results and the empirical choice of parameters. In this paper, we propose a family of loss functions named \textit{Leveraged Weighted} (LW) loss, which for the first time introduces the leverage parameter $\beta$ to consider the trade-off between losses on partial labels and non-partial ones. From the theoretical side, we derive a generalized result of risk consistency for the LW loss in learning from partial labels, based on which we provide guidance to the choice of the leverage parameter $\beta$. In experiments, we verify the theoretical guidance, and show the high effectiveness of our proposed LW loss on both benchmark and real datasets compared with other state-of-the-art partial label learning algorithms.

中文翻译：

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部分标签学习的杠杆加权损失

作为弱监督学习的一个重要分支，部分标签学习处理的数据是每个实例都被分配了一组候选标签，而其中只有一个是真的。尽管有许多关于从部分标签中学习的方法论研究，但在相对较弱的假设下，仍然缺乏对它们的风险一致性属性的理论理解，尤其是在理论结果与参数的经验选择之间的联系上。在本文中，我们提出了一系列名为 \textit{Leveraged Weighted} (LW) loss 的损失函数，它首次引入了杠杆参数 $\beta$ 来考虑部分标签上的损失和非标签损失之间的权衡。部分的。从理论方面，我们得出了从部分标签学习中 LW 损失的风险一致性的广义结果，在此基础上，我们为杠杆参数 $\beta$ 的选择提供了指导。在实验中，我们验证了理论指导，并展示了与其他最先进的部分标签学习算法相比，我们提出的 LW 损失在基准和真实数据集上的高效性。