Title :
Independent component analysis: source assessment and separation, a Bayesian approach
Author_Institution :
Neural Res. Group, Imperial Coll. of Sci., Technol. & Med., London, UK
fDate :
6/1/1998 12:00:00 AM
Abstract :
The author presents a method of independent component analysis which assesses the most probable number of source sequences from a larger number of observed sequences and estimates the unknown source sequences and mixing matrix. The estimation of the number of true sources is regarded as a model-order estimation problem and is tackled under a Bayesian paradigm. The method is shown to give good results on both synthetic and real data
Keywords :
Bayes methods; estimation theory; matrix algebra; sequences; signal processing; Bayesian approach; independent component analysis; mixing matrix; model-order estimation problem; separation; source assessment; source sequences; true sources;
Journal_Title :
Vision, Image and Signal Processing, IEE Proceedings -
DOI :
10.1049/ip-vis:19981928
