BSSprep: Whitening of Multivariate Data

In BSSprep: Whitening Data as Preparation for Blind Source Separation

Description

A function for data whitening.

Usage

BSSprep(X)

Arguments

X	A numeric matrix. Missing values are not allowed.

Details

A p-variate Y with T observations is whitened, i.e. Y = S^(-1/2)*(X_t - (1/T)*sum_t(X_t)), for t = 1, …, T, where S is the sample covariance matrix of 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

BSSprep documentation built on March 29, 2021, 5:11 p.m.