Distributed variational Bayesian algorithms for Gaussian mixtures in sensor networks

Authors:
B. Safarinejadian;M. B. Menhaj;M. Karrari
Affiliations:
Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran;Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran;Department of Electrical Engineering, Amirkabir University of Technology, 424 Hafez Ave., Tehran, Iran
Venue:
Signal Processing
Year:
2010

Citing 16
Cited 2

A view of the EM algorithm that justifies incremental, sparse, and other variants

Learning in graphical models
Next century challenges: scalable coordination in sensor networks

MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
Learning Mixtures of Gaussians

FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Robust probabilistic inference in distributed systems

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A kernel-based learning approach to ad hoc sensor network localization

ACM Transactions on Sensor Networks (TOSN)
A robust architecture for distributed inference in sensor networks

IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network

IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Distributed Data Mining in Peer-to-Peer Networks

IEEE Internet Computing
Variational approximations in Bayesian model selection for finite mixture distributions

Computational Statistics & Data Analysis
A Learning-Theory Approach to Sensor Networks

IEEE Pervasive Computing
Inferring parameters and structure of latent variable models by variational bayes

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Distributed EM algorithms for density estimation and clustering in sensor networks

IEEE Transactions on Signal Processing
Association rule mining in peer-to-peer systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey on sensor networks

IEEE Communications Magazine
Cooperative information maximization with Gaussian activation functions for self-organizing maps

IEEE Transactions on Neural Networks
Unsupervised Learning of Gaussian Mixtures Based on Variational Component Splitting

IEEE Transactions on Neural Networks

Distributed consensus filtering for discrete-time nonlinear systems with non-Gaussian noise

Signal Processing
Optimized query-driven appointment routing based on Expectation-Maximization in wireless sensor networks

Journal of Network and Computer Applications

Quantified Score

Hi-index	0.08

Visualization

Abstract

This paper presents a distributed variational Bayesian (DVBA) algorithm for density estimation and clustering in sensor networks. It is assumed that measurements of the sensors can be statistically modeled by a common Gaussian mixture model. The variational approach allows the simultaneous estimate of the component parameters and the model complexity. The DVBA algorithm produces an estimate of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, DVBA can be viewed as a distributed processing approach for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the proposed DVBA is also investigated. The proposed method is then used for environmental monitoring and also distributed target classification. Simulation results approve promising performance of this algorithm.