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Proceedings Paper

Regression in sensor networks: training distributively with alternating projections
Author(s): Joel B. Predd; Sanjeev R. Kulkarni; H. Vincent Poor
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Paper Abstract

Wireless sensor networks (WSNs) have attracted considerable attention in recent years and motivate a host of new challenges for distributed signal processing. The problem of distributed or decentralized estimation has often been considered in the context of parametric models. However, the success of parametric methods is limited by the appropriateness of the strong statistical assumptions made by the models. In this paper, a more flexible nonparametric model for distributed regression is considered that is applicable in a variety of WSN applications including field estimation. Here, starting with the standard regularized kernel least-squares estimator, a message-passing algorithm for distributed estimation in WSNs is derived. The algorithm can be viewed as an instantiation of the successive orthogonal projection (SOP) algorithm. Various practical aspects of the algorithm are discussed and several numerical simulations validate the potential of the approach.

Paper Details

Date Published: 16 September 2005
PDF: 15 pages
Proc. SPIE 5910, Advanced Signal Processing Algorithms, Architectures, and Implementations XV, 591006 (16 September 2005); doi: 10.1117/12.620194
Show Author Affiliations
Joel B. Predd, Princeton Univ. (United States)
Sanjeev R. Kulkarni, Princeton Univ. (United States)
H. Vincent Poor, Princeton Univ. (United States)

Published in SPIE Proceedings Vol. 5910:
Advanced Signal Processing Algorithms, Architectures, and Implementations XV
Franklin T. Luk, Editor(s)

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