SVM-Guided Biplot of Observations and Variables

초록

We consider support vector machines(SVM) to predict Y with p numerical variables X_1,..., 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.

키워드