Regression in sensor networks: Training distributively with alternating projections

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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.

Original languageEnglish (US)
Article number591006
Pages (from-to)1-15
Number of pages15
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2005
EventAdvanced Signal Processing Algorithms, Architectures, and Implementations XV - San Diego, CA, United States
Duration: Aug 2 2005Aug 4 2005

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • Distributed estimation
  • Distributed learning, nonparametric
  • Kernel methods
  • Regression, alternating projections
  • Wireless sensor networks


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