Classifier-independent visualization of supervised data structures using a graph

Abstract

Supervised data structures in high dimensional feature spaces are displayed as graphs. The structure is analyzed by normal mixture distributions. The nodes of the graph correspond the mean vectors of the mixture distributions, and the location is carried out by Sammon's nonlinear mapping. The thickness of the edges expresses the separability between the component distributions, which is determined by KullbackLeibler divergence. Prom experimental results, it was confirmed that the proposed method can illustrate in which regions and to what extent it is difficult to classify samples correctly. Such visual information can be utilized for the improvement of the feature sets. ©Springer-Verlag 2004.