Noisy group testing: An information theoretic perspective
Noisy group testing: An information theoretic perspective
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嘈杂的群体测试：信息论的视角
DOI：
10.1109/allerton.2009.5394787
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Venkatesh Saligrama
中科院分区：
文献类型：
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作者：
George K. Atia;Venkatesh Saligrama
组测试的基本任务是从一个大的组中恢复一个小的可区分的项目子集，同时有效地减少测试（测量）的总数。本文的主要贡献是采用了一种新的信息理论的角度来看群体测试问题。建立其连接到香农编码理论，我们制定的组测试问题作为一个信道编码/解码问题，并得出一个统一的结果，减少了许多有趣的问题，计算的互信息表达式。这个结果是相当一般的;它允许我们验证一些已知场景的现有边界，并将分析扩展到许多新的有趣设置，包括噪声版本的组测试。我们考虑的模型是确定性无噪声的情况下，近似重建有界失真，加性测量噪声，稀释模型。
The fundamental task of group testing is to recover a small distinguished subset of items from a large group while efficiently reducing the total number of tests (measurements). The key contribution of this paper is in adopting a new information-theoretic perspective on group testing problems. Establishing its connection to Shannon-coding theory, we formulate the group testing problem as a channel coding/decoding problem and derive a unifying result that reduces many of the interesting questions to computation of a mutual information expression. This result is fairly general; it allows us to verify existing bounds for some of the known scenarios and extend the analysis to many new interesting setups including noisy versions of group testing. Among the models we consider are the deterministic noise-free case, approximate reconstruction with bounded distortion, additive measurement noise, and dilution models.