TY - JOUR
T1 - Support vector machines with piecewise linear feature mapping
AU - Huang, Xiaolin
AU - Mehrkanoon, Siamak
AU - Suykens, Johan A.K.
N1 - Funding Information:
This work was supported in part by the scholarship of the Flemish Government; Research Council KUL : GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering (OPTEC), IOF-SCORES4CHEM, several PhD/postdoc & fellow grants; Flemish Government: FWO : PhD/postdoc grants, projects: G0226.06 (cooperative systems and optimization), G.0302.07 (SVM/Kernel), G.0320.08 (convex MPC), G.0558.08 (Robust MHE), G.0557.08 (Glycemia2), G.0588.09 (Brain-machine) research communities (WOG: ICCoS, ANMMM, MLDM); G.0377.09 (Mechatronics MPC), G.0377.12 (Structured models), IWT: PhD Grants, Eureka-Flite+, SBO LeCoPro, SBO Climaqs, SBO POM, O&O-Dsquare; Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007–2011); IBBT; EU: ERNSI; ERC AdG A-DATADRIVE-B, FP7-HD-MPC (INFSO-ICT-223854), COST intelliCIS, FP7-EMBOCON (ICT-248940); Contract Research: AMINAL; Other: Helmholtz: viCERP, ACCM, Bauknecht, Hoerbiger. Johan Suykens is a professor at KU Leuven, Belgium.
PY - 2013/10/6
Y1 - 2013/10/6
N2 - As the simplest extension to linear classifiers, piecewise linear (PWL) classifiers have attracted a lot of attention, because of their simplicity and classification capability. In this paper, a PWL feature mapping is introduced by investigating the property of the PWL classification boundary. Then support vector machines (SVM) with PWL feature mappings are proposed, called PWL-SVMs. In this paper, it is shown that some widely used PWL classifiers, such as k-nearest-neighbor, adaptive boosting of linear classifier and intersection kernel support vector machine, can be represented by the proposed feature mapping. That means the proposed PWL-SVMs at least can achieve the performance of the above PWL classifiers. Moreover, PWL-SVMs enjoy good properties of SVM and the performance on numerical experiments illustrates the effectiveness. Then some extensions are discussed and the application of PWL-SVMs can be expected.
AB - As the simplest extension to linear classifiers, piecewise linear (PWL) classifiers have attracted a lot of attention, because of their simplicity and classification capability. In this paper, a PWL feature mapping is introduced by investigating the property of the PWL classification boundary. Then support vector machines (SVM) with PWL feature mappings are proposed, called PWL-SVMs. In this paper, it is shown that some widely used PWL classifiers, such as k-nearest-neighbor, adaptive boosting of linear classifier and intersection kernel support vector machine, can be represented by the proposed feature mapping. That means the proposed PWL-SVMs at least can achieve the performance of the above PWL classifiers. Moreover, PWL-SVMs enjoy good properties of SVM and the performance on numerical experiments illustrates the effectiveness. Then some extensions are discussed and the application of PWL-SVMs can be expected.
KW - Nonlinear feature mapping
KW - Piecewise linear classifier
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=84878903255&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2013.01.023
DO - 10.1016/j.neucom.2013.01.023
M3 - Article
AN - SCOPUS:84878903255
SN - 0925-2312
VL - 117
SP - 118
EP - 127
JO - Neurocomputing
JF - Neurocomputing
ER -