 Learning the optimal kernel for Fisher discriminant analysis via second order cone programmingReshma Khemchandani, Jayadeva and Suresh Chandra European Journal of Operational Research, 2010, vol. 203, issue 3, 692-697 Abstract: Kernel Fisher discriminant analysis (KFDA) is a popular classification technique which requires the user to predefine an appropriate kernel. Since the performance of KFDA depends on the choice of the kernel, the problem of kernel selection becomes very important. In this paper we treat the kernel selection problem as an optimization problem over the convex set of finitely many basic kernels, and formulate it as a second order cone programming (SOCP) problem. This formulation seems to be promising because the resulting SOCP can be efficiently solved by employing interior point methods. The efficacy of the optimal kernel, selected from a given convex set of basic kernels, is demonstrated on UCI machine learning benchmark datasets. Keywords: Fisher; discriminant; analysis; Kernel; methods; Machine; learning; Kernel; optimization; Support; vector; machines; Convex; optimization; Second; order; cone; programming; Semidefinite; programming (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:203:y:2010:i:3:p:692-697 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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