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http://dx.doi.org/10.9708/jksci.2011.16.4.015

A Non-linear Variant of Improved Robust Fuzzy PCA  

Heo, Gyeong-Yong (Visual Media Center, Dongeui University)
Seo, Jin-Seok (Dept. of Game Eng., Dongeui University)
Lee, Im-Geun (Dept. of Visual Information Eng., Dongeui University)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.4, 2011 , pp. 15-22 More about this Journal
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;
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Times Cited By KSCI : 1  (Citation Analysis)
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