Girolami, M; (1998) An alternative perspective on adaptive independent component analysis algorithms. NEURAL COMPUT , 10 (8) 2103 - 2114.
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This article develops an extended independent component analysis algorithm for mixtures of arbitrary subgaussian and supergaussian sources. The gaussian mixture model of Pearson is employed in deriving a closed-form generic score function for strictly subgaussian sources. This is combined with the score function for a unimodal supergaussian density to provide a computationally simple yet powerful algorithm for performing independent component analysis on arbitrary mixtures of nongaussian sources.
|Title:||An alternative perspective on adaptive independent component analysis algorithms|
|Keywords:||BLIND SEPARATION, LEARNING ALGORITHM, SIGNALS|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science|
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