Reconstruction of Gaussian mixture models from compressive measurements: A phase transition view

Francesco Renna, Robert Calderbank, Lawrence Carin, Miguel R.D. Rodrigues

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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. © 2013 IEEE.
Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
DOIs
StatePublished - Dec 1 2013
Externally publishedYes

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