Robust independent component analysis for cognitive informatics

This paper evaluates the outlier sensitivity of five independent component analysis (ICA) algorithms (FastICA, extended-Infomax, JADE, radical, and β-divergence) using: (i) the Amari separation performance index, (ii) the optimum angle of rotation error, and (iii) the contrast function difference, in an outlier-contaminated mixture simulation. The Amari separation performance index has revealed a strong sensitivity of JADE and FastICA, using 3rd- and 4th- order nonlinearities, to outliers. However, the two contrast measures demonstrated conclusively that β-divergence is the least outlier-sensitive algorithm, followed by Radical, FastICA (exponential and hyperbolic-tangent nonlinearities), extended-Infomax, JADE, and FastICA (3rd-and 4th-order nonlinearities) in an outlier-contaminated mixture of two uniformly distributed signals. The novelty of this paper is the development of an unbiased optimization-landscape environment for assessing outlier sensitivity, as well as the optimum angle of rotation error and the contrast function difference as promising new measures for assessing the outlier sensitivity of ICA algorithms.