Toward value difference metric with attribute weighting

Chaoqun Li, Liangxiao Jiang*, Hongwei Li, Jia Wu, Peng Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


In distance metric learning, recent work has shown that value difference metric (VDM) with a strong attribute independence assumption outperforms other existing distance metrics. However, an open question is whether VDM with a less restrictive assumption can perform even better. Many approaches have been proposed to improve VDM by weakening the assumption. In this paper, we make a comprehensive survey on the existing improved approaches and then propose a new approach to improve VDM by attribute weighting. We name the proposed new distance function as attribute-weighted value difference metric (AWVDM). Moreover, we propose a modified attribute-weighted value difference metric (MAWVDM) by incorporating the learned attribute weights into the conditional probability estimates of AWVDM. AWVDM and MAWVDM significantly outperform VDM and inherit the computational simplicity of VDM simultaneously. Experimental results on a large number of UCI data sets validate the performance of AWVDM and MAWVDM.

Original languageEnglish
Pages (from-to)795-825
Number of pages31
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - 1 Mar 2017
Externally publishedYes


  • Attribute weighting
  • Distance metric learning
  • Mutual information
  • Value difference metric

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