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Collaborative Training in Sensor Networks: A graphical model approach
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
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