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