TY - GEN
T1 - Adapted statistical compressive sensing: Learning to sense gaussian mixture models
AU - Duarte-Carvajalino, Julio M.
AU - Yu, Guoshen
AU - Carin, Lawrence
AU - Sapiro, Guillermo
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2012/10/23
Y1 - 2012/10/23
N2 - A framework for learning sensing kernels adapted to signals that follow a Gaussian mixture model (GMM) is introduced in this paper. This follows the paradigm of statistical compressive sensing (SCS), where a statistical model, a GMM in particular, replaces the standard sparsity model of classical compressive sensing (CS), leading to both theoretical and practical improvements. We show that the optimized sensing matrix outperforms random sampling matrices originally exploited both in CS and SCS. © 2012 IEEE.
AB - A framework for learning sensing kernels adapted to signals that follow a Gaussian mixture model (GMM) is introduced in this paper. This follows the paradigm of statistical compressive sensing (SCS), where a statistical model, a GMM in particular, replaces the standard sparsity model of classical compressive sensing (CS), leading to both theoretical and practical improvements. We show that the optimized sensing matrix outperforms random sampling matrices originally exploited both in CS and SCS. © 2012 IEEE.
UR - http://ieeexplore.ieee.org/document/6288708/
UR - http://www.scopus.com/inward/record.url?scp=84867618666&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2012.6288708
DO - 10.1109/ICASSP.2012.6288708
M3 - Conference contribution
SN - 9781467300469
SP - 3653
EP - 3656
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -