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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Adaptive Underdetermined ICA for Handling an Unknown Number of Sources
verfasst von : Andreas Sandmair, Alam Zaib, Fernando Puente León
Erschienen in: Latent Variable Analysis and Signal Separation
Verlag: Springer Berlin Heidelberg
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Independent Component Analysis is the best known method for solving blind source separation problems. In general, the number of sources must be known in advance. In many cases, previous assumption is not justified. To overcome difficulties caused by an unknown number of sources, an adaptive algorithm based on a simple geometric approach for Independent Component Analysis is presented. By adding a learning rule for the number of sources, the complete method is a two-step algorithm, adapting alternately the number of sources and the mixing matrix. The independent components are estimated in a separate source inference step as required for underdetermined mixtures.