Nonparametric decentralized detection and sparse sensor selection via weighted kernel

Weiguang Wang, Yingbin Liang, Eric P. Xing, Lixin Shen

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

The kernel-based nonparametric approach proposed by Nguyen, Wainwright, and Jordan is further investigated for decentralized detection. In contrast with the uniform kernel used in the previous work, a weighted kernel is proposed, where weight parameters serve to selectively incorporate sensors' information into the fusion center's decision rule based on quality of sensors' observations. Furthermore, weight parameters also serve as sensor selection parameters with nonzero parameters corresponding to sensors being selected. By introducing the regularization on weight parameters into the risk minimization framework, sensor selection is jointly performed with decision rules for sensors and the fusion center with the resulting optimal decision rule having only sparse nonzero weight parameters. A gradient projection algorithm and a Gauss-Seidel algorithm are developed to solve the risk minimization problem, which is nonconvex, and both algorithms are shown to converge to critical points. Conditions on the sample complexity to guarantee asymptotically small estimation error are characterized based on analysis of Rademacher complexity. Connection between the probability of error and the risk function is also studied. Numerical results are provided to demonstrate the advantages and properties of the proposed approach based on weighted kernel.

Original languageEnglish (US)
Article number7229364
Pages (from-to)306-321
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume64
Issue number2
DOIs
StatePublished - Jan 15 2016

Keywords

  • Convergence
  • Gauss-Seidel Algorithm
  • Gradient Projection
  • KL-property
  • non-Convex Problem
  • Risk Minimization
  • RKHS
  • sensor selection

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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