TY - GEN
T1 - Generalized thresholding sparsity-aware algorithm for low complexity online learning
AU - Kopsinis, Yannis
AU - Slavakis, Konstantinos
AU - Theodoridis, Sergios
AU - McLaughlin, Steve
PY - 2012
Y1 - 2012
N2 - In this paper, a novel scheme for online, sparsity-aware learning is presented. A new theory is developed that allows for the incorporation, in a unifying way, of different thresholding rules to promote sparsity, that may even be of a nonconvex nature. The complexity of the algorithm exhibits a linear dependence on the number of free parameters.
AB - In this paper, a novel scheme for online, sparsity-aware learning is presented. A new theory is developed that allows for the incorporation, in a unifying way, of different thresholding rules to promote sparsity, that may even be of a nonconvex nature. The complexity of the algorithm exhibits a linear dependence on the number of free parameters.
KW - Adaptive filtering
KW - signal recovery
KW - sparsity
KW - thresholding operators
UR - http://www.scopus.com/inward/record.url?scp=84867602729&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867602729&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2012.6288615
DO - 10.1109/ICASSP.2012.6288615
M3 - Conference contribution
AN - SCOPUS:84867602729
SN - 9781467300469
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 3277
EP - 3280
BT - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
T2 - 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Y2 - 25 March 2012 through 30 March 2012
ER -