Robust Principal Component Analysis Using Statistical Estimators
  Principal Component Analysis (PCA) finds a linear mapping and maximizes the
variance of the data which makes PCA sensitive to outliers and may cause wrong
eigendirection. In this paper, we propose techniques to solve this problem; we
use the data-centering method and reestimate the covariance matrix using robust
statistic techniques such as median, robust scaling which is a booster to
data-centering and Huber M-estimator which measures the presentation of
outliers and reweight them with small values. The results on several real world
data sets show that our proposed method handles outliers and gains better
results than the original PCA and provides the same accuracy with lower
computation cost than the Kernel PCA using the polynomial kernel in
classification tasks.
