  This paper addresses the high dimensionality problem in blind source
separation (BSS), where the number of sources is greater than two. Two pairwise
iterative schemes are proposed to tackle this high dimensionality problem. The
two pairwise schemes realize nonparametric independent component analysis (ICA)
algorithms based on a new high-performance Convex CauchySchwarz Divergence
(CCSDIV). These two schemes enable fast and efficient demixing of sources in
real-world high dimensional source applications. Finally, the performance
superiority of the proposed schemes is demonstrated in metric-comparison with
FastICA, RobustICA, convex ICA (CICA), and other leading existing algorithms.
