Collaborative Training in Sensor Networks: A graphical model approach
Abstract
Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples.
Cite
@article{arxiv.0907.5168,
title = {Collaborative Training in Sensor Networks: A graphical model approach},
author = {Haipeng Zheng and Sanjeev R. Kulkarni and H. Vincent Poor},
journal= {arXiv preprint arXiv:0907.5168},
year = {2016}
}