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.
|Maintainer:||Juha Karvanen <juha.karvanen at iki.fi>|
|Citation:||PearsonICA citation info|
|CRAN checks:||PearsonICA results|
|Windows binaries:||r-devel: PearsonICA_1.2-4.zip, r-release: PearsonICA_1.2-4.zip, r-oldrel: PearsonICA_1.2-4.zip|
|OS X Mavericks binaries:||r-release: PearsonICA_1.2-4.tgz, r-oldrel: PearsonICA_1.2-4.tgz|
|Old sources:||PearsonICA archive|
Please use the canonical form https://CRAN.R-project.org/package=PearsonICA to link to this page.