Sparse sensor selection for nonparametric decentralized detection via L1 regularization

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sensor selection in nonparametric decentralized detection is investigated. Kernel-based minimization framework with a weighted kernel is adopted, where the kernel weight parameters represent sensors' contributions to decision making. L1 regularization on weight parameters is introduced into the risk function so that the resulting optimal decision rule contains a sparse vector of nonzero weight parameters. In this way, sensor selection is naturally performed because only sensors corresponding to nonzero weight parameters contribute to decision making. A gradient projection algorithm and a Gauss-Seidel algorithm are developed to jointly perform weight selection (i.e., sensor selection) and optimize decision rules. Both algorithms are shown to converge to critical points for this non-convex optimization problem. Numerical results are provided to demonstrate the advantages and properties of the proposed sensor selection approach.

Original languageEnglish (US)
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing, MLSP
EditorsTulay Adali, Jan Larsen, Mamadou Mboup, Eric Moreau
PublisherIEEE Computer Society
ISBN (Electronic)9781479936946
DOIs
StatePublished - Nov 14 2014
Event2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 - Reims, France
Duration: Sep 21 2014Sep 24 2014

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014
CountryFrance
CityReims
Period9/21/149/24/14

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ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

Cite this

Wang, W., Liang, Y., Xing, E. P., & Shen, L. (2014). Sparse sensor selection for nonparametric decentralized detection via L1 regularization. In T. Adali, J. Larsen, M. Mboup, & E. Moreau (Eds.), IEEE International Workshop on Machine Learning for Signal Processing, MLSP [6958898] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). IEEE Computer Society. https://doi.org/10.1109/MLSP.2014.6958898