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首页> 外文会议>2013 Information Theory and Applications Workshop >On finding a set of healthy individuals from a large population
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On finding a set of healthy individuals from a large population

机译：从大量人口中找到一组健康个体

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摘要

In this paper, we explore fundamental limits on the number of tests required to identify a given number of “healthy” items from a large population containing a small number of “defective” items, in a nonadaptive group testing framework. Specifically, we derive mutual information-based upper bounds on the number of tests required to identify the required number of healthy items. Our results show that an impressive reduction in the number of tests is achievable compared to the conventional approach of using classical group testing to first identify the defective items and then pick the required number of healthy items from the complement set. For example, to identify L healthy items out of a population of N items containing K defective items, when the tests are reliable, our results show that O(K(L - 1)/(N - K)) measurements are sufficient. In contrast, the conventional approach requires O(K log(N/K)) measurements. We derive our results in a general sparse signal setup, and hence, they are applicable to other sparse signal-based applications such as compressive sensing also.
机译：在本文中，我们探索了在非自适应小组测试框架中从包含少量“缺陷”物品的大量人群中识别给定数量的“健康”物品所需的测试数量的基本限制。具体而言，我们得出了基于互信息的上限，以确定所需的健康项目数量所需的测试数量。我们的结果表明，与传统方法相比，与传统方法相比，使用传统的集体测试首先识别有缺陷的物品，然后从补品集中选择所需数量的健康物品，可以显着减少测试数量。例如，要在可靠的测试中从N个包含K个缺陷项的N个项中识别出L个健康项，我们的结果表明O（K（L-1）/（N-K））个测量就足够了。相反，常规方法需要O（K log（N / K））个测量值。我们在一般的稀疏信号设置中得出结果，因此，它们也适用于其他基于稀疏信号的应用，例如压缩感测。

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