DocumentCode
3254715
Title
Reconstruction of Gaussian mixture models from compressive measurements: A phase transition view
Author
Renna, Francesco ; Calderbank, R. ; Carin, Lawrence ; Rodrigues, Miguel R. D.
Author_Institution
Inst. de Telecomun., Univ. do Porto, Porto, Portugal
fYear
2013
fDate
3-5 Dec. 2013
Firstpage
628
Lastpage
628
Abstract
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.
Keywords
Gaussian processes; compressed sensing; mean square error methods; mixture models; phase transformations; Gaussian mixture models; classification approach; compressive measurement; low-noise regime; minimum mean squared error; phase-transition optimal recovery procedures; reconstruction approach; Educational institutions; Electronic mail; Gaussian mixture model; Image reconstruction; Measurement uncertainty; Phase measurement; Compressive sensing; Gaussian mixture models; minimum mean squared error; phase transition;
fLanguage
English
Publisher
ieee
Conference_Titel
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location
Austin, TX
Type
conf
DOI
10.1109/GlobalSIP.2013.6736965
Filename
6736965
Link To Document