Bayesian Cramer-Rao bounds for factorized model based low rank matrix reconstruction
2015 (English)In: Proceedings of the 24th European Signal Processing Conference (EUSIPCO), 2015, Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper, Poster (Refereed)
Low-rank matrix reconstruction (LRMR) problem considersestimation (or reconstruction) of an underlying low-rank matrixfrom under-sampled linear measurements. A low-rank matrix can be represented using a factorized model. In thisarticle, we derive Bayesian Cramer-Rao bounds for LRMR where a factorized model is used. We first show a general informative bound, and then derive several Bayesian Cramer-Rao bounds for different scenarios. We always considered the low-rank matrix to be reconstructed as a random matrix, but its model hyper-parameters for three cases - deterministic known, deterministic unknown and random. Finally we compare the bounds with existing practical algorithms through numerical simulations.
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015.
Low-rank matrices, matrix completion, Bayesian estimation, Cramer-Rao bounds.
Research subject Electrical Engineering
IdentifiersURN: urn:nbn:se:kth:diva-190111OAI: oai:DiVA.org:kth-190111DiVA: diva2:951514
The 24th European Signal Processing Conference (EUSIPCO), 31st August to 4 of September 2015
QC 201608102016-08-092016-08-092016-08-10Bibliographically approved