RKF-PCA: Robust kernel fuzzy PCA

@article{Heo2009RKFPCARK,
  title={RKF-PCA: Robust kernel fuzzy PCA},
  author={Gyeongyong Heo and Paul D. Gader and Hichem Frigui},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2009},
  volume={22 5-6},
  pages={642-50}
}
Principal component analysis (PCA) is a mathematical method that reduces the dimensionality of the data while retaining most of the variation in the data. Although PCA has been applied in many areas successfully, it suffers from sensitivity to noise and is limited to linear principal components. The noise sensitivity problem comes from the least-squares measure used in PCA and the limitation to linear components originates from the fact that PCA uses an affine transform defined by eigenvectors… CONTINUE READING
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