Whitening_transformation
Whitening transformation
Decorrelation method that converts a covariance matrix of a set of samples into an identity matrix
A whitening transformation or sphering transformation is a linear transformation that transforms a vector of random variables with a known covariance matrix into a set of new variables whose covariance is the identity matrix, meaning that they are uncorrelated and each have variance 1.[1] The transformation is called "whitening" because it changes the input vector into a white noise vector.
Several other transformations are closely related to whitening:
- the decorrelation transform removes only the correlations but leaves variances intact,
- the standardization transform sets variances to 1 but leaves correlations intact,
- a coloring transformation transforms a vector of white random variables into a random vector with a specified covariance matrix.[2]