Classifier-Independent Visualization of Supervised Data Structures Using a Graph

  • Hiroshi Tenmoto
  • Yasukuni Mori
  • Mineichi Kudo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

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 Kullback-Leibler divergence. From 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.

Keywords

Feature Selection Method High Dimensional Feature Space Confusion Matrice Component Distribution Projection Pursuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hiroshi Tenmoto
    • 1
  • Yasukuni Mori
    • 2
  • Mineichi Kudo
    • 3
  1. 1.Kushiro National College of TechnologyKushiro, HokkaidoJapan
  2. 2.Chiba UniversityChibaJapan
  3. 3.Hokkaido UniversitySapporoJapan

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