Collaborative training in sensor networks: A graphical model approach

Haipeng Zheng; Sanjeev R. Kulkarni; H. Vincent Poor

doi:10.1109/MLSP.2009.5306188

Collaborative training in sensor networks: A graphical model approach

Haipeng Zheng, Sanjeev R. Kulkarni, H. Vincent Poor

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

1 Scopus citations

Abstract

Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples.

Original language	English (US)
Title of host publication	Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009
DOIs	https://doi.org/10.1109/MLSP.2009.5306188
State	Published - 2009
Event	Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 - Grenoble, France Duration: Sep 2 2009 → Sep 4 2009

Publication series

Name	Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Other

Other	Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009
Country/Territory	France
City	Grenoble
Period	9/2/09 → 9/4/09

All Science Journal Classification (ASJC) codes

Human-Computer Interaction
Signal Processing
Education

Access to Document

10.1109/MLSP.2009.5306188

Cite this

Zheng, H., Kulkarni, S. R., & Poor, H. V. (2009). Collaborative training in sensor networks: A graphical model approach. In Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009 Article 5306188 (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009). https://doi.org/10.1109/MLSP.2009.5306188

Zheng, Haipeng ; Kulkarni, Sanjeev R. ; Poor, H. Vincent. / Collaborative training in sensor networks : A graphical model approach. Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009).

@inproceedings{b13a7e825d054731a96a60311f8f1724,

title = "Collaborative training in sensor networks: A graphical model approach",

abstract = "Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples.",

author = "Haipeng Zheng and Kulkarni, {Sanjeev R.} and Poor, {H. Vincent}",

note = "Copyright: Copyright 2010 Elsevier B.V., All rights reserved.; Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009 ; Conference date: 02-09-2009 Through 04-09-2009",

year = "2009",

doi = "10.1109/MLSP.2009.5306188",

language = "English (US)",

isbn = "9781424449484",

series = "Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009",

booktitle = "Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009",

}

Zheng, H, Kulkarni, SR & Poor, HV 2009, Collaborative training in sensor networks: A graphical model approach. in Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009., 5306188, Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009, Grenoble, France, 9/2/09. https://doi.org/10.1109/MLSP.2009.5306188

Collaborative training in sensor networks: A graphical model approach. / Zheng, Haipeng; Kulkarni, Sanjeev R.; Poor, H. Vincent.
Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. 5306188 (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009).

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

TY - GEN

T1 - Collaborative training in sensor networks

T2 - Machine Learning for Signal Processing XIX - 2009 IEEE Signal Processing Society Workshop, MLSP 2009

AU - Zheng, Haipeng

AU - Kulkarni, Sanjeev R.

AU - Poor, H. Vincent

PY - 2009

Y1 - 2009

N2 - Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples.

AB - Graphical models have been widely applied in solving distributed inference problems in sensor networks. In this paper, the problem of coordinating a network of sensors to train a unique ensemble estimator under communication constraints is discussed. The information structure of graphical models with specific potential functions is employed, and this thus converts the collaborative training task into a problem of local training plus global inference. Two important classes of algorithms of graphical model inference, message-passing algorithm and sampling algorithm, are employed to tackle low-dimensional, parametrized and high-dimensional, non-parametrized problems respectively. The efficacy of this approach is demonstrated by concrete examples.

UR - http://www.scopus.com/inward/record.url?scp=77950953509&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77950953509&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2009.5306188

DO - 10.1109/MLSP.2009.5306188

M3 - Conference contribution

AN - SCOPUS:77950953509

SN - 9781424449484

T3 - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

BT - Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009

Y2 - 2 September 2009 through 4 September 2009

ER -

Zheng H, Kulkarni SR , Poor HV. Collaborative training in sensor networks: A graphical model approach. In Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009. 2009. 5306188. (Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009). doi: 10.1109/MLSP.2009.5306188

Collaborative training in sensor networks: A graphical model approach

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this