Adaptive underdetermined ICA for handling an unknown number of sources

Authors:
Andreas Sandmair;Alam Zaib;Fernando Puente León
Affiliations:
Karlsruhe Institute of Technology, Institute of Industrial Information Technology, Karlsruhe, Germany;Karlsruhe Institute of Technology, Institute of Industrial Information Technology, Karlsruhe, Germany;Karlsruhe Institute of Technology, Institute of Industrial Information Technology, Karlsruhe, Germany
Venue:
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Year:
2010

Citing 4
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
Linear geometric ICA: fundamentals and algorithms

Neural Computation
Learning Overcomplete Representations

Neural Computation
Estimation of Source Signals Number and Underdetermined Blind Separation Based on Sparse Representation

Computational Intelligence and Security

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.