Aldridge, M orcid.org/0000-0002-9347-1586, Johnson, O and Scarlett, J (2016) Improved group testing rates with constant column weight designs. In: 2016 IEEE International Symposium on Information Theory (ISIT). 2016 IEEE International Symposium on Information Theory (ISIT), 10-15 Jul 2016, Barcelona, Spain. IEEE ISBN 978-1-5090-1807-9
Abstract
We consider nonadaptive group testing where each item is placed in a constant number of tests. The tests are chosen uniformly at random with replacement, so the testing matrix has (almost) constant column weights. We show that performance is improved compared to Bernoulli designs, where each item is placed in each test independently with a fixed probability. In particular, we show that the rate of the practical COMP detection algorithm is increased by 31% in all sparsity regimes. In dense cases, this beats the best possible algorithm with Bernoulli tests, and in sparse cases is the best proven performance of any practical algorithm. We also give an algorithm-independent upper bound for the constant column weight case; for dense cases this is again a 31% increase over the analogous Bernoulli result.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE.This is an author produced version of a paper published in 2016 IEEE International Symposium on Information Theory (ISIT). Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Jul 2020 10:17 |
Last Modified: | 30 Jul 2020 11:32 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/isit.2016.7541525 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163716 |