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conference paper
Multitask diffusion LMS with sparsity-based regularization
2015
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
In this work, a diffusion-type algorithm is proposed to solve multitask estimation problems where each cluster of nodes is interested in estimating its own optimum parameter vector in a distributed manner. The approach relies on minimizing a global mean-square error criterion regularized by a term that promotes piecewise constant transitions in the parameter vector entries estimated by neighboring clusters. We provide some results on the mean and mean-square-error convergence. Simulations are conducted to illustrate the effectiveness of the strategy.
Type
conference paper
Authors
Publication date
2015
Publisher
Published in
International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Start page
3516
End page
3520
Peer reviewed
REVIEWED
EPFL units
Event name | Event place | Event date |
South Brisbane, Queensland, Australia | April 19-24, 2015 | |
Available on Infoscience
December 19, 2017
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