| Type: | Package |
| Title: | Whitening Data as Preparation for Blind Source Separation |
| Version: | 0.1 |
| Date: | 2021-03-25 |
| Maintainer: | Markus Matilainen <markus.matilainen@outlook.com> |
| Imports: | Rcpp (≥ 0.11.0) |
| LinkingTo: | Rcpp, RcppArmadillo |
| Description: | Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| Packaged: | 2021-03-25 14:05:39 UTC; manmat |
| Author: | Markus Matilainen 
[cre, aut],
Klaus Nordhausen
[aut] |
| Repository: | CRAN |
| Date/Publication: | 2021-03-29 09:32:16 UTC |
Whitening Data as Preparation for Blind Source Separation
Description
Whitening is the first step of almost all blind source separation (BSS) methods. A fast implementation of whitening for BSS is implemented to serve as a lightweight dependency for packages providing BSS methods.
Details
| Package: | BSSprep |
| Type: | Package |
| Version: | 0.1 |
| Date: | 2021-03-25 |
| License: | GPL (>= 2) |
This package contains the single function BSSprep for whitening multivariate data as a preprocessing step for blind source separation (BSS). The package is meant as a fast and lightweight dependency for packages providing BSS methods as whitening is almost always the first step.
Author(s)
Markus Matilainen, Klaus Nordhausen
Maintainer: Markus Matilainen <markus.matilainen@outlook.com>
Whitening of Multivariate Data
Description
A function for data whitening.
Usage
BSSprep(X)
Arguments
X |
A numeric matrix. Missing values are not allowed. |
Details
A p-variate {\bf Y} with T observations is whitened, i.e. {\bf Y}={\bf S}^{-1/2}({\bf X}_t - \frac{1}{T}\sum_{t=1}^T {\bf X}_{t}), for t = 1, \ldots, T,
where {\bf S} is the sample covariance matrix of {\bf X}.
This is often need as a preprocessing step like in almost all blind source separation (BSS) methods. The function is implemented using C++ and returns the whitened data matrix as well as the ingredients to back transform.
Value
A list containing the following components:
Y |
The whitened data matrix. |
X.C |
The mean-centered data matrix. |
COV.sqrt.i |
The inverse square root of the covariance matrix of X. |
MEAN |
Mean vector of X. |
Author(s)
Markus Matilainen, Klaus Nordhausen
Examples
n <- 100
X <- matrix(rnorm(10*n) - 1, nrow = n, ncol = 10)
res1 <- BSSprep(X)
res1$Y # The whitened matrix
colMeans(res1$Y) # should be close to zero
cov(res1$Y) # should be close to the identity matrix
res1$MEAN # Should hover around -1 for all 10 columns