R/whiten.R

In decorrelate: Decorrelation Projection Scalable to High Dimensional Data

# Gabriel Hoffman June 1, 2021 whiten() function combines eclairs and
# decorrelate

#' Decorrelation projection + eclairs
#'
#' Efficient decorrelation projection using eclairs decomposition
#'
#' @param X matrix to be transformed so *columns* are independent
#' @param k the rank of the low rank component
#' @param lambda specify lambda and override value estimated by \code{eclairs()}
#'
#' @return data rotated and scaled according to the regularized sample covariance of the input data
#'
#' @examples
#' library(Rfast)
#'
#' n <- 800 # number of samples
#' p <- 200 # number of features
#'
#' # create correlation matrix
#' Sigma <- autocorr.mat(p, .9)
#'
#' # draw data from correlation matrix Sigma
#' Y <- rmvnorm(n, rep(0, p), sigma = Sigma * 5.1, seed = 1)
#'
#' # eclairs decomposition
#' ecl <- eclairs(Y)
#'
#' # whitened Y
#' Y.transform <- decorrelate(Y, ecl)
#'
#' # Combine eclairs and decorrelate into one step
#' Y.transform2 <- whiten(Y)
#'
#' @export
whiten <- function(X, k = ncol(X), lambda = NULL) {
  ecl <- eclairs(X, k, lambda = lambda)

  decorrelate(X, ecl)
}