SVM-Guided Biplot of Observations and Variables

Title & Authors
SVM-Guided Biplot of Observations and Variables
Huh, Myung-Hoe;

Abstract
We consider support vector machines(SVM) to predict Y with p numerical variables $\small{X_1}$, $\small{{\ldots}}$, $\small{X_p}$. This paper aims to build a biplot of p explanatory variables, in which the first dimension indicates the direction of SVM classification and/or regression fits. We use the geometric scheme of kernel principal component analysis adapted to map n observations on the two-dimensional projection plane of which one axis is determined by a SVM model a priori.
Keywords
Support vector machine;kernel trick;principal component analysis;biplot;
Language
English
Cited by
1.
Global and Local Views of the Hilbert Space Associated to Gaussian Kernel,;

Communications for Statistical Applications and Methods, 2014. vol.21. 4, pp.317-325
1.
Global and Local Views of the Hilbert Space Associated to Gaussian Kernel, Communications for Statistical Applications and Methods, 2014, 21, 4, 317
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