BSSprep {BSSprep} | R Documentation |

## 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
```

[Package

*BSSprep*version 0.1 Index]