TY - GEN
T1 - Sparse Gaussian mixture detection
T2 - 2017 IEEE International Symposium on Information Theory, ISIT 2017
AU - Ligo, Jonathan G.
AU - Moustakides, George V.
AU - Veeravalli, Venugopal Varadachari
N1 - ACKNOWLEDGMENT This work was supported by the US National Science Foundation under the grant CIF 1514245 through the University of Illinois at Urbana-Champaign, and under the Grant CIF 1513373, through Rutgers University.
PY - 2017/8/9
Y1 - 2017/8/9
N2 - We study the problem of testing between a sparse signal in noise, modeled as a mixture distribution, versus pure noise, with a Gaussian signal and noise of same variance, but differing means as the mixture proportion tends to zero. We construct a simple new adaptive test based on quantizing data with sample size-dependent quantizers and prove its consistency. The proposed test has almost linear time complexity and sublinear space complexity, which is better than existing tests, and in particular, the celebrated Higher Criticism test. Moreover, our numerical results show that the proposed test is competitive with commonly used tests even with a small number of quantizer levels.
AB - We study the problem of testing between a sparse signal in noise, modeled as a mixture distribution, versus pure noise, with a Gaussian signal and noise of same variance, but differing means as the mixture proportion tends to zero. We construct a simple new adaptive test based on quantizing data with sample size-dependent quantizers and prove its consistency. The proposed test has almost linear time complexity and sublinear space complexity, which is better than existing tests, and in particular, the celebrated Higher Criticism test. Moreover, our numerical results show that the proposed test is competitive with commonly used tests even with a small number of quantizer levels.
KW - Detection theory
KW - Gaussian mixture model
KW - Quantization
KW - Sparse detection
KW - Sparse mixture
UR - http://www.scopus.com/inward/record.url?scp=85034049419&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85034049419&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2017.8006734
DO - 10.1109/ISIT.2017.8006734
M3 - Conference contribution
AN - SCOPUS:85034049419
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1277
EP - 1281
BT - 2017 IEEE International Symposium on Information Theory, ISIT 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 June 2017 through 30 June 2017
ER -