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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Bibliographische Detailangaben

Hauptverfasser:	Forero, P.A., Cano, A., Giannakis, G.B.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	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
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