TY - GEN
T1 - Detection of correlated components in multivariate Gaussian models
AU - Geng, Jun
AU - Xu, Weiyu
AU - Lai, Lifeng
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/12/17
Y1 - 2015/12/17
N2 - In this paper, the problem of detecting correlated components in a p-dimensional Gaussian vector is considered. In the setup considered, s unknown components are correlated with a known covariance structure. Hence, there are equation possible hypotheses for the unknown set of correlated components. Instead of taking a full-vector observation at each time index, in this paper we assume that the observer is capable of observing any subset of components in the vector. With this flexibility in taking observations, the observer is interested in finding the optimal sampling strategy to maximize the error exponent (per sample) of the multi-hypothesis testing problem. We show that, when the correlation of these s components is weak, it is optimal for the observer to take full-vector observations; when the correlation is strong, the strategy of taking full-vector observation is not optimal anymore, and the optimal sampling strategy increases the detection error exponent by 25% at least, compared with the full-vector observation strategy.
AB - In this paper, the problem of detecting correlated components in a p-dimensional Gaussian vector is considered. In the setup considered, s unknown components are correlated with a known covariance structure. Hence, there are equation possible hypotheses for the unknown set of correlated components. Instead of taking a full-vector observation at each time index, in this paper we assume that the observer is capable of observing any subset of components in the vector. With this flexibility in taking observations, the observer is interested in finding the optimal sampling strategy to maximize the error exponent (per sample) of the multi-hypothesis testing problem. We show that, when the correlation of these s components is weak, it is optimal for the observer to take full-vector observations; when the correlation is strong, the strategy of taking full-vector observation is not optimal anymore, and the optimal sampling strategy increases the detection error exponent by 25% at least, compared with the full-vector observation strategy.
KW - detection of correlation
KW - error exponent
KW - optimal sampling strategy
KW - spiked signal model
UR - https://www.scopus.com/pages/publications/84962647739
U2 - 10.1109/ITWF.2015.7360768
DO - 10.1109/ITWF.2015.7360768
M3 - 会议稿件
AN - SCOPUS:84962647739
T3 - ITW 2015 - 2015 IEEE Information Theory Workshop
SP - 224
EP - 228
BT - ITW 2015 - 2015 IEEE Information Theory Workshop
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Information Theory Workshop, ITW 2015
Y2 - 11 October 2015 through 15 October 2015
ER -