Title :
A greedy sparsity-promoting LMS for distributed adaptive learning in diffusion networks
Author :
Chouvardas, Symeon ; Mileounis, Gerasimos ; Kalouptsidis, Nicholas ; Theodoridis, S.
Author_Institution :
Dept. of Inf. & Telecommun., Univ. of Athens, Ilisia, Greece
Abstract :
In this paper, a distributed adaptive algorithm for sparsity-aware learning in diffusion networks is developed. The algorithm follows the greedy roadmap for sparsity along with the adapt-combine co-operation strategy, based on the LMS rationale for adaptivity. A bound on the error norm between the obtained estimates and the target vector is computed, and the algorithm is shown to converge in the mean under some general assumptions. Finally, comparative experiments with a recently developed sparsity-promoting diffusion LMS demonstrate the enhanced performance of the proposed algorithm.
Keywords :
distributed processing; greedy algorithms; learning (artificial intelligence); LMS rationale; adapt-combine co-operation strategy; adaptivity; diffusion networks; distributed adaptive algorithm; distributed adaptive learning; error norm; greedy roadmap; greedy sparsity-promoting LMS; sparsity-aware learning; sparsity-promoting diffusion LMS; target vector; Adaptive systems; Convergence; Least squares approximations; Network topology; Signal processing algorithms; Topology; Vectors; Adaptive distributed learning; Greedy techniques; Sparsity-aware learning;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
Conference_Location :
Vancouver, BC
DOI :
10.1109/ICASSP.2013.6638698