Conference proceeding
Detection of correlated components in multivariate Gaussian models
2015 IEEE Information Theory Workshop - Fall (ITW), pp.224-228
10/2015
DOI: 10.1109/ITWF.2015.7360768
Abstract
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.
Details
- Title: Subtitle
- Detection of correlated components in multivariate Gaussian models
- Creators
- Jun Geng - Harbin Institute of TechnologyWeiyu Xu - University of IowaLifeng Lai - Worcester Polytechnic Institute
- Resource Type
- Conference proceeding
- Publication Details
- 2015 IEEE Information Theory Workshop - Fall (ITW), pp.224-228
- DOI
- 10.1109/ITWF.2015.7360768
- Publisher
- IEEE
- Language
- English
- Date published
- 10/2015
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197304402771
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