Toward value difference metric with attribute weighting

Publication Type:
Journal Article
Knowledge and Information Systems, 2017, 50 (3), pp. 795 - 825
Issue Date:
Full metadata record
Files in This Item:
Filename Description Size
Li2017_Article_TowardValueDifferenceMetricWit.pdfPublished Version1.07 MB
Adobe PDF
© 2016, Springer-Verlag London. 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.
Please use this identifier to cite or link to this item: