The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Distributed EM algorithms for density estimation and clustering in sensor networks
Abstract
The paper considers the problem of density estimation and clustering in distributed sensor networks. It is assumed that each node in the network senses an environment that can be described as a mixture of some elementary conditions. The measurements are thus statistically modeled with a mixture of Gaussians, where each Gaussian component corresponds to one of the elementary conditions. The paper presents a distributed expectation-maximization (EM) algorithm for estimating the Gaussian components, which are common to the environment and sensor network as a whole, as well as the mixing probabilities that may vary from node to node. The algorithm produces an estimate (in terms of a Gaussian mixture approximation) of the density of the sensor data without requiring the data to be transmitted to and processed at a central location. Alternatively, the algorithm can be viewed as a distributed processing strategy for clustering the sensor data into components corresponding to predominant environmental features sensed by the network. The convergence of the distributed EM algorithm is investigated, and simulations demonstrate the potential of this approach to sensor network data analysis.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The capacity of wireless networks
Gupta, P., & Kumar, P. R. (2000) · IEEE Transactions on Information Theory · doi:10.1109/18.825799 · Cited by 8,292
The EM Algorithm and Extensions
Kushary, D. (1998) · Technometrics · doi:10.1080/00401706.1998.10485534 · Cited by 5,417
Maximum likelihood estimation from incomplete data via the EM algorithm
Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,543
On the Statistical Analysis of Dirty Pictures
Besag, J. (1986) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1986.tb01412.x · Cited by 4,191
On the Convergence Properties of the EM Algorithm
Wu, C. F. J. (1983) · The Annals of Statistics · doi:10.1214/aos/1176346060 · Cited by 3,269
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Unsupervised learning of finite mixture models
Figueiredo, M. A. T., & Jain, A. K. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.990138 · Cited by 2,125
The EM Algorithm and Extensions, 2E
McLachlan, G. J., & Krishnan, T. (2008) · Wiley Series in Probability and Statistics · doi:10.1002/9780470191613 · Cited by 1,529
On Convergence Properties of the EM Algorithm for Gaussian Mixtures
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Cited by
Distributed optimization in sensor networks
Rabbat, M., & Nowak, R. (2004) · Proceedings of the 3rd international symposium on Information processing in sensor networks · doi:10.1145/984622.984626 · Cited by 901
Self-citationOn Distributed Convex Optimization Under Inequality and Equality Constraints
Zhu, M., & Martinez, S. (2012) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2011.2167817 · Cited by 759
Information fusion for wireless sensor networks: Methods, models, and classifications
Nakamura, E. F., Loureiro, A. A. F., & Frery, A. C. (2007) · ACM Computing Surveys · doi:10.1145/1267070.1267073 · Cited by 537
A Convergent Incremental Gradient Method with a Constant Step Size
Blatt, D., Hero, A. O., & Gauchman, H. (2007) · SIAM Journal on Optimization · doi:10.1137/040615961 · Cited by 306
Metadata
- Authors
- Nowak, R. D.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2003-08
- Volume
- 51
- Issue
- 8
- Pages
- 2245-2253
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2158307424
- Cited by
- 256
- References
- 15
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
