Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component. We propose a novel unsupervised criterion, based on SVD-entropy, selecting a feature according to its contribution to the entropy (CE) calculated on a leave-one-out basis. This can be implemented in four ways: simple ranking according to CE values (SR), forward selection by accumulating features according to which set produces highest entropy (FS1), forward selection by accumulating features through the choice of the best CE out of the remaining ones (FS2), backward elimination (BE) of features with the lowest CE.