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Applicability of ICA-Based Dimension Reduction in Fuzzy c-Means-Based Classifier

  • Takuya Kobayashi
  • Katsuhiro Honda
  • Akira Notsu
  • Hidetomo Ichihashi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8228)

Abstract

Fuzzy c-Means-based Classifier (FCMC) has been proved to have high performances based on clustering concepts in conjunction with several parameter optimization methods. In general, FCMC is applied to high-dimensional data after dimension reduction by Principal Component Analysis (PCA). In this paper, the applicability of Independent Component Analysis (ICA)-based dimension reduction is investigated in the FCMC context. ICA is a computational method for separating a multivariate signal into additive subcomponents with the assumption of non-Gaussian signals. This paper compares the performance of FCMC using four data sets. Two initialization approaches of the PCA-Tree-based and k-dimensional tree (kd-Tree)-based are also compared.

Keywords

Classifier Clustering Principal component analysis Independent component analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Takuya Kobayashi
    • 1
  • Katsuhiro Honda
    • 1
  • Akira Notsu
    • 1
  • Hidetomo Ichihashi
    • 2
  1. 1.Department of Computer Science and Intelligent SystemsOsaka Prefecture UniversityOsakaJapan
  2. 2.Faculty of EconomicsOsaka University of Economics and LawOsakaJapan

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