Minimum Support ICA Using Order Statistics. Part II: Performance Analysis

Linear instantaneous independent component analysis (ICA) is a well-known problem, for which efficient algorithms like FastICA and JADE have been developed. Nevertheless, the development of new contrasts and optimization procedures is still needed, e.g. to improve the separation performances in specific cases. For example, algorithms may exploit prior information, such as the sparseness or the non-negativity of the sources. In this paper, we show that support-width minimizationbased ICA algorithms may outperform other well-known ICA methods when extracting bounded sources. The output supports are estimated using symmetric differences of order statistics.