Finite-horizon quickest search in correlated high-dimensional data

Saeid Balaneshin, Ali Tajer, H. Vincent Poor

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

3 Scopus citations

Abstract

The problem of searching over a large number of data streams for identifying one that holds certain features of interest is considered. The data streams are assumed to be generated by one of two possible statistical distributions with cumulative distribution functions F0 and F 1 and the objective is to identify one sequence generated by F 1 as quickly as possible, and prior to a pre-specified deadline. Furthermore, it is assumed that the generation of the data streams follows a known dependency kernel such that the likelihood of a sequence being generated by F1 depends on the underlying distributions of the other data streams. The optimal sequential sampling strategy is characterized, and numerical evaluations are provided to illustrate the gains of incorporating the information about the dependency structure into the design of the sampling process.

Original languageEnglish (US)
Title of host publicationISCCSP 2014 - 2014 6th International Symposium on Communications, Control and Signal Processing, Proceedings
PublisherIEEE Computer Society
Pages222-225
Number of pages4
ISBN (Print)9781479928903
DOIs
StatePublished - Jan 1 2014
Event6th International Symposium on Communications, Control and Signal Processing, ISCCSP 2014 - Athens, Greece
Duration: May 21 2014May 23 2014

Publication series

NameISCCSP 2014 - 2014 6th International Symposium on Communications, Control and Signal Processing, Proceedings

Other

Other6th International Symposium on Communications, Control and Signal Processing, ISCCSP 2014
CountryGreece
CityAthens
Period5/21/145/23/14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing

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