The Effect of Outliers in Independent Component Analysis

This is the authors' abstract. We don't add key points for this paper.

SYNOPTIC ABSTRACTIndependent Component Analysis (ICA) is a statistical and computational technique for decomposing a complex multivariate data into independent components. Several methods have been proposed to find the independent components and they all assume the homogeneity (free of outliers) of the data, which is almost never true in practice. In this study, we propose an algorithm to improve ICA performance in the presence of outliers by introducing an additional step in the data pre-processing. We also show, by using the simulated and real mixed data sets, that the proposed algorithm provides a significant improvement in finding more accurate independent components in the presence of outliers.