A Non-linear Variant of Improved Robust Fuzzy PCA (잡음 민감성이 향상된 주성분 분석 기법의 비선형 변형)

허경용; JINSEOK SEO (서진석); Lee, Im Geun (이임건)

A Non-linear Variant of Improved Robust Fuzzy PCA

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2011, 16(4), pp.15-22
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

허경용 ¹, JINSEOK SEO ¹, Lee, Im Geun ¹

¹동의대학교

Accredited

ABSTRACT

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction while maintaining most of the variation in data. Although PCA has been applied in many areas successfully, it is sensitive to outliers and only valid for Gaussian distributions. Several variants of PCA have been proposed to resolve noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA, however, is still a linear algorithm that cannot accommodate non-Gaussian distributions. In this paper, a non-linear algorithm that combines RF-PCA2 and kernel PCA (K-PCA), called improved robust kernel fuzzy PCA (RKF-PCA2), is introduced. The kernel methods make it to accommodate non-Gaussian distributions. RKF-PCA2 inherits noise robustness from RF-PCA2 and non-linearity from K-PCA. RKF-PCA2 outperforms previous methods in handling non-Gaussian distributions in a noise robust way. Experimental results also support this.

KEYWORDS

Principal Component Analysis, Noise Sensitivity, Kernel Trick, Non-linear Property, Kernel Principal Component Analysis

Citation status

* References for papers published after 2023 are currently being built.

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