Independent Component Analysis (ICA) Using Pearsonian Density Function

Authors:
Abhijit Mandal;Arnab Chakraborty
Affiliations:
Indian Statistical Institute, Kolkata, India 700108;Statistical Consultant, Kolkata, India 700108
Venue:
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Year:
2009

Citing 3
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Independent component analysis: algorithms and applications

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Independent component analysis (ICA) is an important topic of signal processing and neural network which transforms an observed multidimensional random vector into components that are mutually as independent as possible. In this paper, we have introduced a new method called SwiPe-ICA (S tepwi se Pe arsonian ICA) that combines the methodology of projection pursuit with Pearsonian density estimation. Pearsonian density function instead of the classical polynomial density expansions is employed to approximate the density along each one-dimensional projection using differential entropy. This approximation of entropy is more exact than the classical approximation based on the polynomial density expansions when the source signals are supergaussian. The validity of the new algorithm is verified by computer simulation.