Computer Science – Distributed – Parallel – and Cluster Computing
Scientific paper
2009-07-29
Proceedings of IEEE International Workshop on Machine Learning for Signal Processing, 2009
Computer Science
Distributed, Parallel, and Cluster Computing
Scientific paper
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.
Kulkarni Sanjeev R.
Poor Harold Vincent
Zheng Haipeng
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