  We present a novel technique, called DSVP (Discrimination through Singular
Vectors Projections), to discriminate spurious events within a dataset. The
purpose of this paper is to lay down a general procedure which can be tailored
for a broad variety of applications. After describing the general concept, we
apply the algorithm to the problem of identifying nearly coincident events in
low temperature microcalorimeters in order to push the time resolution close to
its intrinsic limit. In fact, from simulated datasets it was possible to
achieve an effective time resolution even shorter than the sampling time of the
system considered. The obtained results are contextualized in the framework of
the HOLMES experiment, which aims at directly measuring the neutrino mass with
the calorimetric approach, allowing to significally improve its statistical
sensitivity.
