Multi-label learning deals with data examples which are associated with multiple class labels simultaneously. Despite the success of existing approaches to multi-label learning, there is still a problem neglected by researchers, i.e., not only are some of the values of observed labels missing, but also some of the labels are completely unobserved for the training data. We refer to the problem as multi-label learning with missing and completely unobserved labels, and argue that it is necessary to discover these completely unobserved labels in order to mine useful knowledge and make a deeper understanding of what is behind the data. In this paper, we propose a new approach named MCUL to solve multi-label learning with Missing and Completely Unobserved Labels. We try to discover the unobserved labels of a multi-label data set with a clustering based regularization term and describe the semantic meanings of them based on the label-specific features learned by MCUL, and overcome the problem of missing labels by exploiting label correlations. The proposed method MCUL can predict both the observed and newly discovered labels simultaneously for unseen data examples. Experimental results validated over ten benchmark datasets demonstrate that the proposed method can outperform other state-of-the-art approaches on observed labels and obtain an acceptable performance on the new discovered labels as well.

多标签学习处理与多个类别标签同时关联的数据示例。尽管现有的多标签学习方法取得了成功，但研究人员仍然忽略了一个问题，即，不仅观察到的标签的某些值丢失了，而且训练数据也完全没有观察到某些标签。我们将问题称为缺少标签和完全未观察到的多标签学习，并认为有必要发现这些完全未被观察到的标签，以便挖掘有用的知识并更深入地了解数据背后的原因。在本文中，我们提出了一种名为MCUL的新方法，以解决缺少标签和完全未被观察到的标签的多标签学习。我们尝试使用基于聚类的正则化术语来发现多标签数据集的未观察标签，并基于MCUL学习的标签特定功能描述它们的语义，并通过利用标签相关性来克服缺少标签的问题。所提出的方法MCUL可以同时预测观察到的标签和新发现的标签，以获取看不见的数据示例。