TY - GEN
T1 - Accelerated reconstruction of a compressively sampled data stream
AU - Sopasakis, Pantelis
AU - Freris, Nikolaos
AU - Patrinos, Panagiotis
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/28
Y1 - 2016/11/28
N2 - The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed: the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. In this paper, we develop a novel Newton-type forwardbackward proximal method to recursively solve the regularized Least-Squares problem (LASSO) online. We establish global convergence of our method as well as a local quadratic convergence rate. Our simulations show a substantial speed-up over the state of the art which may render the proposed method suitable for applications with stringent real-time constraints.
AB - The traditional compressed sensing approach is naturally offline, in that it amounts to sparsely sampling and reconstructing a given dataset. Recently, an online algorithm for performing compressed sensing on streaming data was proposed: the scheme uses recursive sampling of the input stream and recursive decompression to accurately estimate stream entries from the acquired noisy measurements. In this paper, we develop a novel Newton-type forwardbackward proximal method to recursively solve the regularized Least-Squares problem (LASSO) online. We establish global convergence of our method as well as a local quadratic convergence rate. Our simulations show a substantial speed-up over the state of the art which may render the proposed method suitable for applications with stringent real-time constraints.
KW - Compressed sensing
KW - Forward backward splitting
KW - LASSO
KW - Operator splitting methods
KW - Recursive algorithms
UR - http://www.scopus.com/inward/record.url?scp=85006049678&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006049678&partnerID=8YFLogxK
U2 - 10.1109/EUSIPCO.2016.7760414
DO - 10.1109/EUSIPCO.2016.7760414
M3 - Conference contribution
AN - SCOPUS:85006049678
T3 - European Signal Processing Conference
SP - 1078
EP - 1082
BT - 2016 24th European Signal Processing Conference, EUSIPCO 2016
PB - European Signal Processing Conference, EUSIPCO
T2 - 24th European Signal Processing Conference, EUSIPCO 2016
Y2 - 28 August 2016 through 2 September 2016
ER -