GAUSS_DD Gaussian data description.
W = GAUSS_DD(A,FRACREJ,R)
Fit a Gaussian density on dataset A. If requested, the r can be
given to add some regularization to the estimated covariance matrix:
sig_new = (1-r)*sig + r*eye(dim). Default r = 0.01!!! (might be
dangerous!)
This version actually computes just the Mahalanobis distance to the
mean. This should avoid underflows at the computation of a real Gaussian
density (especially problematic in high dimensional spaces).
See also: mcd_gauss_dd rob_gauss_dd |mappings| dd_roc
mcd_gauss_dd
Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
Faculty EWI, Delft University of Technology
P.O. Box 5031, 2600 GA Delft, The Netherlands