  The problem of filtering information from large correlation matrices is of
great importance in many applications. We have recently proposed the use of the
Kullback-Leibler distance to measure the performance of filtering algorithms in
recovering the underlying correlation matrix when the variables are described
by a multivariate Gaussian distribution. Here we use the Kullback-Leibler
distance to investigate the performance of filtering methods based on Random
Matrix Theory and on the shrinkage technique. We also present some results on
the application of the Kullback-Leibler distance to multivariate data which are
non Gaussian distributed.
