Distance Metric Learning via Iterated Support Vector Machines

Wangmeng Zuo, Faqiang Wang, David Zhang, Liang Lin, Yuchi Huang, Deyu Meng, Lei Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

33 Citations (Scopus)


Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely positive-semidefinite constrained metric learning (PCML) and nonnegative-coefficient constrained metric learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification, and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

Original languageEnglish
Article number7973168
Pages (from-to)4937-4950
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number10
Publication statusPublished - 1 Oct 2017


  • alternating minimization
  • kernel method
  • Lagrange duality
  • Metric learning
  • support vector machine

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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