TY - GEN
T1 - Reconstruction of Gaussian mixture models from compressive measurements: A phase transition view
AU - Renna, Francesco
AU - Calderbank, Robert
AU - Carin, Lawrence
AU - Rodrigues, Miguel R.D.
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2013/12/1
Y1 - 2013/12/1
N2 - We characterize the minimum number of measurements needed to drive to zero the minimum mean squared error (MMSE) of Gaussian mixture model (GMM) input signals in the low-noise regime. The result also hints at almost phase-transition optimal recovery procedures based on a classification and reconstruction approach. © 2013 IEEE.
AB - We characterize the minimum number of measurements needed to drive to zero the minimum mean squared error (MMSE) of Gaussian mixture model (GMM) input signals in the low-noise regime. The result also hints at almost phase-transition optimal recovery procedures based on a classification and reconstruction approach. © 2013 IEEE.
UR - http://ieeexplore.ieee.org/document/6736965/
UR - http://www.scopus.com/inward/record.url?scp=84897732176&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2013.6736965
DO - 10.1109/GlobalSIP.2013.6736965
M3 - Conference contribution
SN - 9781479902484
BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
ER -