Distributed EM algorithms for density estimation and clustering in sensor networks
Top Cited Papers
- 15 July 2003
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 51 (8) , 2245-2253
- https://doi.org/10.1109/tsp.2003.814623
Abstract
The paper considers the problem of density estimation and clustering in distributed sensor networks. It is assumed that each node in the network senses an environment that can be described as a mixture of some elementary conditions. The measurements are thus statistically modeled with a mixture of Gaussians, where each Gaussian component corresponds to one of the elementary conditions. The paper presents a distributed expectation-maximization (EM) algorithm for estimating the Gaussian components, which are common to the environment and sensor network as a whole, as well as the mixing probabilities that may vary from node to node. The algorithm produces an estimate (in terms of a Gaussian mixture approximation) of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, the algorithm can be viewed as a distributed processing strategy for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the distributed EM algorithm is investigated, and simulations demonstrate the potential of this approach to sensor network data analysis.Keywords
This publication has 11 references indexed in Scilit:
- Unsupervised learning of finite mixture modelsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002
- Distributed compression in a dense microsensor networkIEEE Signal Processing Magazine, 2002
- Accelerating EM for Large DatabasesMachine Learning, 2001
- Asymptotic Convergence Rate of the EM Algorithm for Gaussian MixturesNeural Computation, 2000
- The capacity of wireless networksIEEE Transactions on Information Theory, 2000
- On-line EM Algorithm for the Normalized Gaussian NetworkNeural Computation, 2000
- A View of the Em Algorithm that Justifies Incremental, Sparse, and other VariantsPublished by Springer Nature ,1998
- On Convergence Properties of the EM Algorithm for Gaussian MixturesNeural Computation, 1996
- On the Statistical Analysis of Dirty PicturesJournal of the Royal Statistical Society Series B: Statistical Methodology, 1986
- On the Convergence Properties of the EM AlgorithmThe Annals of Statistics, 1983