@inproceedings{40c2ec1a35f24ffa819dc17987271728,
title = "Learning distributed jointly sparse systems by collaborative LMS",
abstract = "In the proposed model of adaptive filtering network, distributed learning algorithm works cooperatively to identify separated unknown systems, which have different impulse responses. Specifically, JS-CoLMS algorithm is proposed to iteratively learn the unknown systems and the joint sparsity, based on a stochastic gradient approach and a subdifferentiable sparse-inducing penalty approximating the l2,0 norm. The superior performance of the proposed algorithm and its relation to l0-LMS and Leaky LMS are briefly discussed and verified by numerical experiments.",
keywords = "Collaborative LMS, Distributed learning, Distributed optimization, JS-CoLMS, Leaky LMS, adaptive filtering network, joint sparsity",
author = "Yuantao Gu and Mengdi Wang",
year = "2014",
doi = "10.1109/ICASSP.2014.6855003",
language = "English (US)",
isbn = "9781479928927",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7228--7232",
booktitle = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014",
address = "United States",
note = "2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 ; Conference date: 04-05-2014 Through 09-05-2014",
}