NDLI: A fault tolerant peer-to-peer distributed EM algorithm

Content Provider	IEEE Xplore Digital Library
Author	Safarinejadian, B. Menhaj, M.B. Karrari, M.
Copyright Year	2009
Description	Author affiliation: Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran (Safarinejadian, B.; Menhaj, M.B.; Karrari, M.)
Abstract	In this paper, a distributed Expectation Maximization (EM) algorithm is proposed for estimating parameters of a Gaussian mixture model in a peer-to-peer network. This algorithm is used for density estimation and clustering of data distributed over nodes of a network. Scalability and fault tolerance are two important advantages of this method. In the E-step of this algorithm, each node calculates local sufficient statistics using its local observations. A peer-to-peer algorithm is then used to diffuse local sufficient statistics to neighboring nodes and estimate global sufficient statistics in each node. In the M-step, each node updates parameters of the Gaussian mixture model using the estimated global sufficient statistics. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve promising performance of this algorithm.
Starting Page	46
Ending Page	52
File Size	497853
Page Count	7
File Format	PDF
ISBN	9781424427659
DOI	10.1109/CIDM.2009.4938628
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault tolerance Parameter estimation Peer to peer computing Clustering algorithms Statistical distributions Distributed databases Gaussian processes Gaussian distribution Data mining Association rules
Content Type	Text
Resource Type	Article

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