Multitask diffusion LMS with sparsity-based regularization

Roula Nassif; Cédric Richard; André Ferrari; Ali H. Sayed

ICASSP 2015accepted0 citations

Multitask diffusion LMS with sparsity-based regularization

Roula Nassif, Cédric Richard, André Ferrari, Ali H. Sayed

Abstract

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.

BibTeX

@inproceedings{icassp2015_multitaskdiffusi,
  title = {Multitask diffusion LMS with sparsity-based regularization},
  author = {Roula Nassif and Cédric Richard and André Ferrari and Ali H. Sayed},
  booktitle = {ICASSP 2015},
  year = {2015}
}