@inproceedings{6334b479673a4c0c89d5ef29f79d94d9,
title = "Detection of sparse mixtures: The finite alphabet case",
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.",
keywords = "Detection theory, error exponents, large deviations, likelihood ratio test, sparse detection",
author = "Ligo, {Jonathan G.} and Moustakides, {George V.} and Veeravalli, {Venugopal V.}",
year = "2017",
month = mar,
day = "1",
doi = "10.1109/ACSSC.2016.7869572",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1243--1247",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016",
note = "50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 ; Conference date: 06-11-2016 Through 09-11-2016",
}