Unsupervised algorithms for distributed estimation over adaptive networks

M. O. Bin Saeed; A. Zerguine; S. A. Zummo; A. H. Sayed

doi:10.1109/ACSSC.2012.6489340

Unsupervised algorithms for distributed estimation over adaptive networks

M. O. Bin Saeed^*, A. Zerguine, S. A. Zummo, A. H. Sayed

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This work shows how to develop distributed versions of block blind estimation techniques that have been proposed before for batch processing. Using diffusion adaptation techniques, data are accumulated at the nodes to form estimates of the auto-correlation matrices and to carry out local SVD and/or Cholesky decomposition steps. Local estimates at neighborhoods are then aggregated to provide online streaming estimates of the parameters of interest. Simulation results illustrate the performance of the algorithms.

Original language	English
Title of host publication	Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Pages	1780-1783
Number of pages	4
DOIs	https://doi.org/10.1109/ACSSC.2012.6489340
State	Published - 2012

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Keywords

Blind estimation
Cholesky factorization
SVD
auto-correlation matrix
diffusion strategy

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.2012.6489340

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Bin Saeed, M. O., Zerguine, A., Zummo, S. A., & Sayed, A. H. (2012). Unsupervised algorithms for distributed estimation over adaptive networks. In Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 (pp. 1780-1783). Article 6489340 (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2012.6489340

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title = "Unsupervised algorithms for distributed estimation over adaptive networks",

abstract = "This work shows how to develop distributed versions of block blind estimation techniques that have been proposed before for batch processing. Using diffusion adaptation techniques, data are accumulated at the nodes to form estimates of the auto-correlation matrices and to carry out local SVD and/or Cholesky decomposition steps. Local estimates at neighborhoods are then aggregated to provide online streaming estimates of the parameters of interest. Simulation results illustrate the performance of the algorithms.",

keywords = "Blind estimation, Cholesky factorization, SVD, auto-correlation matrix, diffusion strategy",

author = "{Bin Saeed}, {M. O.} and A. Zerguine and Zummo, {S. A.} and Sayed, {A. H.}",

year = "2012",

doi = "10.1109/ACSSC.2012.6489340",

language = "English",

isbn = "9781467350518",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

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Bin Saeed, MO, Zerguine, A , Zummo, SA & Sayed, AH 2012, Unsupervised algorithms for distributed estimation over adaptive networks. in Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012., 6489340, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 1780-1783. https://doi.org/10.1109/ACSSC.2012.6489340

Unsupervised algorithms for distributed estimation over adaptive networks. / Bin Saeed, M. O.; Zerguine, A.; Zummo, S. A. et al.
Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012. 2012. p. 1780-1783 6489340 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zerguine, A.

AU - Zummo, S. A.

AU - Sayed, A. H.

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AB - This work shows how to develop distributed versions of block blind estimation techniques that have been proposed before for batch processing. Using diffusion adaptation techniques, data are accumulated at the nodes to form estimates of the auto-correlation matrices and to carry out local SVD and/or Cholesky decomposition steps. Local estimates at neighborhoods are then aggregated to provide online streaming estimates of the parameters of interest. Simulation results illustrate the performance of the algorithms.

KW - Blind estimation

KW - Cholesky factorization

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KW - auto-correlation matrix

KW - diffusion strategy

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ER -

Unsupervised algorithms for distributed estimation over adaptive networks

Abstract

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Keywords

ASJC Scopus subject areas

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