Multi-label learning with missing and completely unobserved labels

Jun Huang, Linchuan Xu, Kun Qian, Jing Wang, Kenji Yamanishi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1061-1086
Number of pages26
JournalData Mining and Knowledge Discovery
Issue number3
Publication statusPublished - May 2021


  • Completely unobserved labels
  • Discovering new labels
  • Missing labels
  • Multi-label learning
  • Unseen labels

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computer Networks and Communications

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