Detection of sparse mixtures: The finite alphabet case

Jonathan G. Ligo, George V. Moustakides, Venugopal V. Veeravalli

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

Abstract

We study the problem of testing between a sparse signal in noise, modeled as a mixture distribution, versus pure noise, with finite alphabet observations. We study the consistency and adaptivity of the tests as the mixture proportion tends to zero with number of observations. The finite alphabet assumption allows for application to inherently categorical data, where no useful ordering relationship on the alphabet typically exists. We construct and analyze a divergence-based adaptive test for finite alphabets and validate it on a quantized Gaussian signal detection problem.

Original languageEnglish (US)
Title of host publicationConference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1243-1247
Number of pages5
ISBN (Electronic)9781538639542
DOIs
StatePublished - Mar 1 2017
Event50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States
Duration: Nov 6 2016Nov 9 2016

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/TerritoryUnited States
CityPacific Grove
Period11/6/1611/9/16

Keywords

  • Detection theory
  • error exponents
  • large deviations
  • likelihood ratio test
  • sparse detection

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Detection of sparse mixtures: The finite alphabet case'. Together they form a unique fingerprint.

Cite this