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arXiv · 0907.5168

Collaborative Training in Sensor Networks: A graphical model approach

Haipeng Zheng·Sanjeev R. Kulkarni·H. Vincent Poor
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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.

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Haipeng Zheng, Sanjeev R. Kulkarni, H. Vincent Poor. 2009-07-29. Collaborative Training in Sensor Networks: A graphical model approach. https://doi.org/10.1109/mlsp.2009.5306188

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