Partial Multi-Label learning (PML) aims to learn from training data where each example is associated with a set of candidate labels, among which only a subset of them is correct. The major challenge of PML lies in that the training procedure is prone to be misled by the label noise. To address this problem, nearly all existing PML methods focus on solely label disambiguation, i.e., dislodging the noisy labels from the candidate label set and then utilizing the remaining credible labels for model induction. However, these remaining “credible” labels may be incorrectly identified, which thereby would have a huge adverse impact on the subsequent model induction. In this paper, in contrary to the above label disambiguation strategy, we propose a simple yet effective Noisy lAbel Tolerated pArtial multi-label Learning (NATAL) method, where the labeling information is considered to be precise while the feature information is assumed to be missing. Using our proposed method, the task of PML can be re-interpreted as a Feature Completion problem, and the desired prediction model can be directly induced from the completed feature together with all candidate labels. Extensive experimental results on various data sets clearly demonstrate the effectiveness of our proposed approach.

部分多标签学习（PML）旨在从训练数据中学习，其中每个示例都与一组候选标签相关联，其中只有一部分是正确的。PML的主要挑战在于训练过程容易被标签噪声所误导。为了解决这个问题，几乎所有现有的PML方法都只关注标签的消歧，即从候选标签集中消除嘈杂的标签，然后利用剩余的可信标签进行模型归纳。但是，这些剩余的“可信”标签可能会被错误地识别，从而对随后的模型归纳产生巨大的不利影响。在本文中，与上述标签消除歧义策略相反，我们提出了一个简单而有效的N oisy l A belŤ olerated p甲rtial多标记大号盈利（NATAL）的方法，其中所述标记信息被认为是精确的假定，而特征信息被丢失。使用我们提出的方法，可以将PML的任务重新解释为特征完成问题，并且可以从完成的特征以及所有候选标签中直接导出所需的预测模型。在各种数据集上的大量实验结果清楚地证明了我们提出的方法的有效性。