PearsonICA: Independent component analysis using score functions from the Pearson system

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.

Author
Juha Karvanen
Date of publication
2009-06-29 09:39:47
Maintainer
Juha Karvanen <juha.karvanen@iki.fi>
License
GPL-2
Version
1.2-4

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Man pages

PearsonICA
Pearson-ICA algorithm for independent component analysis...
PearsonICAdemo
Demonstration of the Pearson-ICA algorithm

Files in this package

PearsonICA
PearsonICA/COPYING
PearsonICA/DESCRIPTION
PearsonICA/inst
PearsonICA/inst/CITATION
PearsonICA/man
PearsonICA/man/PearsonICA.Rd
PearsonICA/man/PearsonICAdemo.Rd
PearsonICA/NAMESPACE
PearsonICA/R
PearsonICA/R/PearsonICA.R