Multilabel relationship learning

Yu ZHANG*, Dit Yan Yeung

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

39 Citations (Scopus)


Multilabel learning problems are commonly found in many applications. A characteristic shared by many multilabel learning problems is that some labels have significant correlations between them. In this article, we propose a novel multilabel learning method, called MultiLabel Relationship Learning (MLRL), which extends the conventional support vector machine by explicitly learning and utilizing the relationships between labels. Specifically, we model the label relationships using a label covariance matrix and use it to define a new regularization term for the optimization problem. MLRL learns the model parameters and the label covariance matrix simultaneously based on a unified convex formulation. To solve the convex optimization problem, we use an alternating method in which each subproblem can be solved efficiently. The relationship between MLRL and two widely used maximum margin methods for multilabel learning is investigated. Moreover, we also propose a semisupervised extension of MLRL, called SSMLRL, to demonstrate how to make use of unlabeled data to help learn the label covariance matrix. Through experiments conducted on some multilabel applications, we find that MLRL not only gives higher classification accuracy but also has better interpretability as revealed by the label covariance matrix.

Original languageEnglish
Article number2499910
JournalACM Transactions on Knowledge Discovery from Data
Issue number2
Publication statusPublished - Jul 2013

Scopus Subject Areas

  • Computer Science(all)

User-Defined Keywords

  • Label relationship
  • Multilabel learning


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