An alternative perspective on adaptive independent component analysis algorithms.
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
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
Archive Staff Only