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.
| Version: |
1.2-5 |
| Imports: |
grDevices, graphics, stats |
| Published: |
2022-02-21 |
| Author: |
Juha Karvanen |
| Maintainer: |
Juha Karvanen <juha.karvanen at iki.fi> |
| License: |
AGPL-3 |
| NeedsCompilation: |
no |
| Citation: |
PearsonICA citation info |
| CRAN checks: |
PearsonICA results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=PearsonICA
to link to this page.