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Bayesian optimization for sensor set selection

By R. Garnett, M. A. Osborne and S. J. Roberts

Abstract

We consider the problem of selecting an optimal set of sensors, as determined, for example, by the predictive accuracy of the resulting sensor network. Given an underlying metric between pairs of set elements, we introduce a natural metric between sets of sensors for this task. Using this metric, we can construct covariance functions over sets, and thereby perform Gaussian process inference over a function whose domain is a power set. If the function has additional inputs, our covariances can be readily extended to incorporate them—allowing us to consider, for example, functions over both sets and time. These functions can then be optimized using Gaussian process global optimization (GPGO). We use the root mean squared error (RMSE) of the predictions made using a set of sensors at a particular time as an example of such a function to be optimized; the optimal point specifies the best choice of sensor locations. We demonstrate the resulting method by dynamically selecting the best subset of a given set of weather sensors for the prediction of the air temperature across the United Kingdom

Topics: Algorithms, Experimentation, Theory Keywords Bayesian methods, Gaussian processes, global optimization, experimental design
Publisher: ACM
Year: 2010
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.193
Provided by: CiteSeerX
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