SVM-Guided Biplot of Observations and Variables Huh, Myung-Hoe;
We consider support vector machines(SVM) to predict Y with p numerical variables , , . 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.
Support vector machine;kernel trick;principal component analysis;biplot;