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Neural Computing and Applications

Erschienen in:

01.01.2015 | Original Article

Projection twin SMMs for 2d image data classification

verfasst von: Haitao Xu, Liya Fan, Xizhan Gao

Erschienen in: Neural Computing and Applications | Ausgabe 1/2015

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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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Titel: Projection twin SMMs for 2d image data classification
verfasst von: Haitao Xu
Liya Fan
Xizhan Gao
Publikationsdatum: 01.01.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 1/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1700-3

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