Consensus-based distributed expectation-maximization algorithm for density estimation and classification using wireless sensor networks

The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on loca...

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Vydáno v:2008 IEEE International Conference on Acoustics, Speech and Signal Processing s. 1989 - 1992
Hlavní autoři: Forero, P.A., Cano, A., Giannakis, G.B.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.03.2008
Témata:
Additive noise
Closed-form solution
Distributed Consensus
Distributed Estimation
Expectation-Maximization
Expectation-maximization algorithms
Gaussian noise
Local government
Maximum likelihood estimation
Mixture
Parameter estimation
Sensor Networks
Sensor phenomena and characterization
Statistical distributions
Wireless sensor networks
ISBN:9781424414833, 1424414830
ISSN:1520-6149
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Shrnutí:The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on local information available to individual sensors, while during the M-step sensors exchange information only with their one- hop neighbors to reach consensus and eventually percolate the global information needed to estimate the wanted parameters across the wireless sensor network (WSN). Analysis and simulations demonstrate that the resultant consensus-based distributed EM (CB-DEM) algorithm matches well the resource- limited characteristics of WSNs and compares favorably with existing alternatives because it has wider applicability and remains resilient to inter-sensor communication noise.
ISBN:9781424414833
1424414830
ISSN:1520-6149
DOI:10.1109/ICASSP.2008.4518028