Distributed Gaussian Mixture Learning Based on Variational Approximations
In this paper, the problem of density estimation and clustering in sensor networks is considered. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. We develop a distributed variational Bayesian algorithm (DVBA) to estimate the parameters of this model. This algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. To verify performance of DVBA, we perform several simulations of sensor networks.
KeywordsSensor Network Density Estimation Gaussian Component Association Rule Mining Finite Mixture
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