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 minimization-based ICA algorithms may outperform other well-known ICA methods when extracting bounded sources. The output supports are estimated using symmetric differences of order statistics. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Vrins, F., & Verleysen, M. (2006). Minimum support ICA using order statistics. Part II: Performance analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 270–277). https://doi.org/10.1007/11679363_34
Mendeley helps you to discover research relevant for your work.