Low-rank matrix reconstruction (LRMR) considers estimation (or reconstruction) of an underlying low-rank matrix from linear measurements. A low-rank matrix can be represented using a factorized model. In this article, we derive Bayesian Cramer-Rao bounds for LRMR where a factorized model is used. We first show a general informative bound, and then derive Bayesian Cramer-Rao bounds for different scenarios. We consider a low-rank random matrix model with hyper-parameters that are — deterministic known, deterministic unknown and random. Finally we compare the bounds with existing estimation algorithms through numerical simulations.
['Martin Sundin', 'Saikat Chatterjee', 'Magnus Jansson']
Bayesian Cramer-Rao bounds for factorized model based low rank matrix reconstruction