Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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SVM-Guided Biplot of Observations and Variables

  • Received : 2013.10.11
  • Accepted : 2013.11.05
  • Published : 2013.11.30
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Abstract

We consider support vector machines(SVM) to predict Y with p numerical variables $X_1$, ${\ldots}$, $X_p$. 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.

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Acknowledgement

Supported by : Korea University

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  1. Global and Local Views of the Hilbert Space Associated to Gaussian Kernel vol.21, pp.4, 2014, https://doi.org/10.5351/CSAM.2014.21.4.317