%0 Journal Article
%T Collaborative Training in Sensor Networks: A graphical model approach
%A Haipeng Zheng
%A Sanjeev R. Kulkarni
%A H. Vincent Poor
%J Computer Science
%D 2009
%I arXiv
%X 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.
%U http://arxiv.org/abs/0907.5168v1