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SVM-Guided Biplot of Observations and VariablesSVM-Guided Biplot of Observations and Variables
- Other Titles
- SVM-Guided Biplot of Observations and Variables
- Authors
- 허명회
- Issue Date
- 2013
- Publisher
- 한국통계학회
- Keywords
- Support vector machine; kernel trick; principal component analysis; biplot.
- Citation
- Communications for Statistical Applications and Methods, v.20, no.6, pp.491 - 498
- Indexed
- KCI
- Volume
- 20
- Number
- 6
- Start Page
- 491
- End Page
- 498
- ISSN
- 2287-7843
- Abstract
- 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.
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