Stochastic Geometry and Random Matrix Theory in Compressed Sensing

Localisation: Université d'Édimbourg, Royaume-Uni
Type: Cours doctoraux
Site: Hors LAMA , IHP
Amphithéâtre Darboux
Date de début:
22/06/2011 - 09:00
Date de fin:
22/06/2011 - 09:00

Stochastic geometry and random matrix theory can be used to model and analyse the efficacy of a new paradigm in signal processing, compressed sensing. This new perspective shows that if a signal is sufficiently simple, it can be acquired at a rate proportional to its information content rather than the worst case rate dictated by a larger ambient space containing it. These lectures will show how this phenomenon can be modelled using stochastic geometry, and will also show how standard eigen-analysis in random matrix theory can give similar results.