Nonparametric decentralized detection based on weighted count kernel

Jiayao Hu, Yingbin Liang, Eric P. Xing

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

1 Scopus citations

Abstract

The nonparametric decentralized detection problem is investigated, in which the joint distribution of the environmental event and the sensors' observations are not known and only a set of training samples are available. The system features rate constraints, i.e., integer bit constraints on sensors' transmissions, different qualities of observations, additional observations to the fusion center, and multi-level tree-structured network. Our study adopts the kernel-based nonparametric approach proposed by Nguyen, Wainwright, and Jordan with the following generalization. A weighted count kernel is introduced so that the corresponding reproducing kernel Hilbert space (RKHS) (over which the fusion center's decision rule is optimized) allows the fusion center's decision rule to count information from sensors and its own observations differently. In order to find the optimal decision rules, our optimization is solved by alternatively and recursively conducting three optimization steps: finding the optimal weight parameters in the weighted count kernel for selecting the best associated RKHS, finding the best optimal decision rule for the fusion center over the identified RKHS, and finding the local decision rules for sensors. Generalization to multilevel tree-structured networks is also discussed. Finally numerical results are provided to demonstrate the performance based on the proposed weighted count kernel.

Original languageEnglish (US)
Title of host publication2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012
Pages324-328
Number of pages5
DOIs
StatePublished - Oct 22 2012
Event2012 IEEE International Symposium on Information Theory, ISIT 2012 - Cambridge, MA, United States
Duration: Jul 1 2012Jul 6 2012

Publication series

NameIEEE International Symposium on Information Theory - Proceedings

Other

Other2012 IEEE International Symposium on Information Theory, ISIT 2012
CountryUnited States
CityCambridge, MA
Period7/1/127/6/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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  • Cite this

    Hu, J., Liang, Y., & Xing, E. P. (2012). Nonparametric decentralized detection based on weighted count kernel. In 2012 IEEE International Symposium on Information Theory Proceedings, ISIT 2012 (pp. 324-328). [6284201] (IEEE International Symposium on Information Theory - Proceedings). https://doi.org/10.1109/ISIT.2012.6284201