PearsonICAdemo

numsig

signal_length

<p>Displays source signals, mixed signals and signals separated by Pearson-ICA.</p>

multivariate

neural

The Pearson-ICA algorithm is a mutual information-based
method for blind separation of statistically independent source
signals. It has been shown that the minimization of mutual
information leads to iterative use of score functions, i.e.
derivatives of log densities. The Pearson system allows
adaptive modeling of score functions. The flexibility of the
Pearson system makes it possible to model a wide range of
source distributions including asymmetric distributions. The
algorithm is designed especially for problems with asymmetric
sources but it works for symmetric sources as well.

Juha Karvanen

PearsonICA

Independent Component Analysis using Score Functions from the
Pearson System

PearsonICAdemo function

Demonstration of the Pearson-ICA Algorithm — PearsonICAdemo

Demonstration of the Pearson-ICA Algorithm

PearsonICAdemo: Demonstration of the Pearson-ICA Algorithm

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Examples