caa a22 4500 606159010 CHVBK 20210128100622.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s00521-014-1700-3 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1700-3 Projection twin SMMs for 2d image data classification [Elektronische Daten] [Haitao Xu, Liya Fan, Xizhan Gao] 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. The Natural Computing Applications Forum, 2014 Projection twin support matrix machine nationallicence Twin support tensor machine nationallicence Projection support tensor machine nationallicence Matrix kernel function nationallicence Iterative algorithm nationallicence Xu Haitao School of Mathematics Sciences, Liaocheng University, 252059, Liaocheng, People's Republic of China aut Fan Liya School of Mathematics Sciences, Liaocheng University, 252059, Liaocheng, People's Republic of China aut Gao Xizhan School of Mathematics Sciences, Liaocheng University, 252059, Liaocheng, People's Republic of China aut Neural Computing and Applications Springer London 26/1(2015-01-01), 91-100 0941-0643 26:1<91 2015 26 521 https://doi.org/10.1007/s00521-014-1700-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1700-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Xu Haitao School of Mathematics Sciences, Liaocheng University, 252059, Liaocheng, People's Republic of China aut NATIONALLICENCE 700 1- Fan Liya School of Mathematics Sciences, Liaocheng University, 252059, Liaocheng, People's Republic of China aut NATIONALLICENCE 700 1- Gao Xizhan School of Mathematics Sciences, Liaocheng University, 252059, Liaocheng, People's Republic of China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/1(2015-01-01), 91-100 0941-0643 26:1<91 2015 26 521