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A Mahalanobis metric learning-based polynomial kernel for classification of hyperspectral images

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Abstract

In this paper, to combine the advantage of both polynomial kernel and the Mahalanobis distance metric learning (DML) methods, we propose a Mahalanobis DML based polynomial kernel for the classification of hyperspectral images. To ensure the method is computing-saving, we adapt a fast iterative method to learn the Mahalanobis matrix. Simulation experiment is conducted on two real hyperspectral data sets. To evaluate the proposed method, we compare it with the traditional radial basis function (RBF) kernel, polynomial kernel and the RBF-based Mahalanobis kernel, the result shows the performance of the proposed method did improve the capability of the polynomial kernel and also perform better than the RBF-based Mahalanobis kernel.

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Acknowledgments

This work is supported by Program for New Century Excellent Talents in University under Grant No. NCET-13-0168 and National Science Foundation of China under Grant No. 61371178.

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Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, 150080, China

    Li Li, Chao Sun, Lianlei Lin, Junbao Li & Shouda Jiang

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  1. Li Li
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  2. Chao Sun
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  3. Lianlei Lin
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  4. Junbao Li
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  5. Shouda Jiang
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Correspondence to Chao Sun.

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Li, L., Sun, C., Lin, L. et al. A Mahalanobis metric learning-based polynomial kernel for classification of hyperspectral images. Neural Comput & Applic 29, 1103–1113 (2018). https://doi.org/10.1007/s00521-016-2499-x

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