Conference proceeding
Identifying correlated components in high-dimensional multivariate Gaussian models
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6424-6428
03/2017
DOI: 10.1109/ICASSP.2017.7953393
Abstract
In this paper, the problem of identifying correlated components in a high-dimensional Gaussian vector is considered. In the setup considered, instead of having to take a full-vector observation at each time index, the observer is allowed to observe any subset or full set of components in the vector, and he has the freedom to design his sampling strategies over time. The observer aims to find an optimal sampling strategy and a decision rule to maximize the error exponent (per sample). We focus on sequential strategies, in which the sampling actions depend on the observations taken so far. We first derive performance bounds of any sequential sampling strategy. We then design a low complexity procedure called sequential diagonal procedure. We show that this low complexity sequential procedure substantially outperforms the optimal non-adaptive strategy when the strength of the signal is strong.
Details
- Title: Subtitle
- Identifying correlated components in high-dimensional multivariate Gaussian models
- Creators
- Jun Geng - Harbin Institute of TechnologyWeiyu Xu - University of IowaLifeng Lai - University of California, Davis
- Resource Type
- Conference proceeding
- Publication Details
- 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6424-6428
- Publisher
- IEEE
- DOI
- 10.1109/ICASSP.2017.7953393
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Language
- English
- Date published
- 03/2017
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197439902771
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