Metric learning with relative distance constraints : a modified SVM approach

C. Luo, M. Li, H. Zhang, F. Wang, Dapeng Zhang, W. Zuo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Distance metric learning plays an important role in many machine learning tasks. In this paper, we propose a method for learning a Mahanalobis distance metric. By formulating the metric learning problem with relative distance constraints, we suggest a Relative Distance Constrained Metric Learning (RDCML) model which can be easily implemented and effectively solved by a modified support vector machine (SVM) approach. Experimental results on UCI datasets and handwritten digits datasets show that RDCML achieves better or comparable classification accuracy when compared with the state-of-the-art metric learning methods.
Original languageEnglish
Title of host publicationIntelligent Computation in Big Data Era
PublisherSpringer Berlin Heidelberg
Number of pages8
ISBN (Print)9783662462478
Publication statusPublished - 2015


  • Kernel method
  • Lagrange duality
  • Mahalanobis distance
  • Metric learning
  • Support vector machine


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