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Neural Processing Letters

, Volume 50, Issue 3, pp 2899–2923 | Cite as

Learning Distance Metric for Support Vector Machine: A Multiple Kernel Learning Approach

  • Weiqi Zhang
  • Zifei YanEmail author
  • Gang Xiao
  • Hongzhi Zhang
  • Wangmeng Zuo
Article
  • 65 Downloads

Abstract

Recent work in distance metric learning has significantly improved the performance in k-nearest neighbor classification. However, the learned metric with these methods cannot adapt to the support vector machines (SVM), which are amongst the most popular classification algorithms using distance metrics to compare samples. In order to investigate the possibility to develop a novel model for joint learning distance metric and kernel classifier, in this paper, we provide a new parameterization scheme for incorporating the squared Mahalanobis distance into the Gaussian RBF kernel, and formulate kernel learning into a generalized multiple kernel learning framework, gearing towards SVM classification. We demonstrate the effectiveness of the proposed algorithm on the UCI machine learning datasets of varying sizes and difficulties and two real-world datasets. Experimental results show that the proposed model achieves competitive classification accuracies and comparable execution time by using spectral projected gradient descent optimizer compared with state-of-the-art methods.

Keywords

Metric learning Multiple kernel learning Gaussian RBF kernel Support vector machines 

Notes

Acknowledgements

This work is partly support by the National Science Foundation of China (NSFC) Project under the Contract Nos. 61671182, 61102037, 61471146 and 61871381.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.No. 211 Hospital of PLAHarbinChina

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