Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21710
Title: Metric learning with relative distance constraints : a modified SVM approach
Authors: Luo, C
Li, M
Zhang, H
Wang, F
Zhang, D 
Zuo, W
Keywords: Kernel method
Lagrange duality
Mahalanobis distance
Metric learning
Support vector machine
Issue Date: 2015
Publisher: Springer Berlin Heidelberg
Source: In Intelligent Computation in Big Data Era, p. 242-249. Springer Berlin Heidelberg, 2015 How to cite?
Abstract: 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.
Description: International Conference of Young Computer Scientists, Engineers and Educators, ICYCSEE 2015, Harbin, 10-12 January 2015
URI: http://hdl.handle.net/10397/21710
ISBN: 978-3-662-46247-8
DOI: 10.1007/978-3-662-46248-5_30
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