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A Variant of Improved Robust Fuzzy PCA

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2011, 16(2), pp.25-32
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

김성훈 1 허경용 2 Woo, Young Woon 2

1경북대학교
2동의대학교

Accredited

ABSTRACT

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction. Although PCA has been applied in many areas successfully, it is sensitive to outliers due to the use of sum-square-error. Several variants of PCA have been proposed to resolve the noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA2, however, still can fall into a local optimum due to equal initial membership values for all data points. Another reason comes from the fact that RF-PCA2 is based on sum-square-error although fuzzy memberships are incorporated. In this paper, a variant of RF-PCA2 called RF-PCA3 is proposed. The proposed algorithm is based on the objective function of RF-PCA2. RF-PCA3 augments RF-PCA2 with the objective function of PCA and initial membership calculation using data distribution, which make RF-PCA3 to have more chance to converge on a better solution than that of RF-PCA2. RF-PCA3 outperforms RF-PCA2, which is demonstrated by experimental results.

Citation status

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