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Projection twin SMMs for 2d image data classification

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Abstract

In this paper, we propose a matrix version extension for linear regularization projection twin support vector machine presented by Shao et al. (Knowl Based Syst 37:203–210, 2013), named as linear projection twin support matrix machine [linear projection twin support matrix machine (PTSMM)], for 2d image data classification. In order to discuss the nonlinear version of PTSMM, a new matrix kernel function is introduced and based on which, we provide a nonlinear PTSMM algorithm with a detailed theoretical derivation. To examine the effectiveness of the presented linear and nonlinear PTSMM, we perform comparative experiments with three linear classifiers support tensor machines, twin support tensor machine and proximal support tensor machine on ORL, YALE and AR databases. Experimental results show that our methods are effective and efficient.

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Authors and Affiliations

  1. School of Mathematics Sciences, Liaocheng University, Liaocheng, 252059, People’s Republic of China

    Haitao Xu, Liya Fan & Xizhan Gao

Authors
  1. Haitao Xu
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  2. Liya Fan
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  3. Xizhan Gao
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Corresponding author

Correspondence to Liya Fan.

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Xu, H., Fan, L. & Gao, X. Projection twin SMMs for 2d image data classification. Neural Comput & Applic 26, 91–100 (2015). https://doi.org/10.1007/s00521-014-1700-3

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  • DOI: https://doi.org/10.1007/s00521-014-1700-3

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