@inproceedings{52e26169614045ddaf6f963e25be9121,
title = "Adaptive clustering for multitask diffusion networks",
abstract = "Diffusion LMS was originally conceived for online distributed parameter estimation in single-task environments where agents pursue a common objective. However, estimating distinct but correlated objects (multitask problems) is useful in many applications. To address multitask problems with combine-then-adapt diffusion LMS strategies, we derive an unsupervised strategy that allows each node to continuously select the neighboring nodes with which it should exchange information to improve its estimation accuracy. Simulation experiments illustrate the efficiency of this clustering strategy. In particular, nodes do not know which other nodes share similar objectives.",
keywords = "adaptive network, combine-then-adapt, Diffusion LMS, distributed learning, multitask problems, online learning",
author = "Jie Chen and Cedric Richard and Sayed, {Ali H.}",
note = "Publisher Copyright: {\textcopyright} 2015 EURASIP.; 23rd European Signal Processing Conference, EUSIPCO 2015 ; Conference date: 31-08-2015 Through 04-09-2015",
year = "2015",
month = dec,
day = "22",
doi = "10.1109/EUSIPCO.2015.7362373",
language = "英语",
series = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "200--204",
booktitle = "2015 23rd European Signal Processing Conference, EUSIPCO 2015",
}