A Fault Tolerant Peer-to-Peer Distributed EM Algorithm

被引：3

作者：

Safarinejadian, Behrooz ^{[1
]}

Menhaj, Mohammad B. ^{[1
]}

Karrari, Mehdi ^{[1
]}

机构：

[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran

来源：

2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING | 2009年

关键词：

D O I：

10.1109/CIDM.2009.4938628

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：46 / 52

页数：7

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