A Data-Driven Quantization Design for Distributed Testing Against Independence with Communication Constraints

This paper studies the problem of designing a quantizer (encoder) for the task of distributed detection of independence subject to one-side communication (limited bits) constraints. By exploiting the asymptotic performance limits as an objective to train a quantization scheme, we propose an algorith...

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Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 5238 - 5242
Main Authors Espinosa, Sebastian, Silva, Jorge F., Piantanida, Pablo
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.05.2022
Subjects
Conferences
Distributed databases
Distributed decision
information bottleneck
lossy data compression
Machine learning
Machine learning algorithms
Quantization (signal)
quantization design
Signal processing algorithms
Task analysis
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ISSN2379-190X
DOI10.1109/ICASSP43922.2022.9746197

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Summary:This paper studies the problem of designing a quantizer (encoder) for the task of distributed detection of independence subject to one-side communication (limited bits) constraints. By exploiting the asymptotic performance limits as an objective to train a quantization scheme, we propose an algorithm that addresses an info-max problem for this lossy compression task. Tools from machine learning are incorporated to facilitate our data-driven optimization. Experiments on synthetic data support our design principle and approximations, expressing that the devised solutions are effective in compressing data while preserving the relevant information for the underlying task of testing against independence.
ISSN:2379-190X
DOI:10.1109/ICASSP43922.2022.9746197