IEEE Transactions on Neural Networks
Average consensus based scalable robust filtering for sensor network
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Journal of Network and Computer Applications
Distributed data mining patterns and services: an architecture and experiments
Concurrency and Computation: Practice & Experience
Massively parallel expectation maximization using graphics processing units
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
Location Feature Integration for Clustering-Based Speech Separation in Distributed Microphone Arrays
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Robust estimation of a global Gaussian mixture by decentralized aggregations of local models
Web Intelligence and Agent Systems
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This paper presents a distributed expectation-maximization (EM) algorithm over sensor networks. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbors and estimate global sufficient statistics in each node. By using this consensus filter, each node can gradually diffuse its local information over the entire network and asymptotically the estimate of global sufficient statistics is obtained. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximize the log-likelihood in the same way as in the standard EM algorithm. Because the consensus filter only requires that each node communicate with its neighbors, the distributed EM algorithm is scalable and robust. It is also shown that the distributed EM algorithm is a stochastic approximation to the standard EM algorithm. Thus, it converges to a local maximum of the log-likelihood. Several simulations of sensor networks are given to verify the proposed algorithm.