分布式贪心算法中的民主投票原则分析
Analysis of Democratic Voting Principles used in Distributed Greedy Algorithms
摘要：
任何用于压缩感知的贪婪追踪算法的一个关键方面是一个好的支持集检测方法。对于分布式压缩感知，我们考虑了一种设置，其中许多传感器测量稀疏信号，这些信号通过信号的交叉支持集的存在而相关。这个相交支撑集被称为联合支撑集。联合支持集的估计对分布式贪婪追踪算法的性能有很大的影响。这种估计可以通过交换本地支持集估计，然后通过（共识）投票方法来实现。在本文中，我们努力的概率分析两个民主投票原则，我们称之为多数和共识投票。在我们的分析中，我们首先模型的输入/输出关系的贪心算法（本地执行的传感器）由一个单一的参数称为未命中概率。在此模型的基础上，我们分析了投票原则，证明了民主投票原则在联合支持集检测方面的优点。
英文摘要：
A key aspect for any greedy pursuit algorithm used in compressed sensing is a good support-set detection method. For distributed compressed sensing, we consider a setup where many sensors measure sparse signals that are correlated via the existence of a signals' intersection support-set. This intersection support-set is called the joint support-set. Estimation of the joint support-set has a high impact on the performance of a distributed greedy pursuit algorithm. This estimation can be achieved by exchanging local support-set estimates followed by a (consensus) voting method. In this paper we endeavor for a probabilistic analysis of two democratic voting principle that we call majority and consensus voting. In our analysis, we first model the input/output relation of a greedy algorithm (executed locally in a sensor) by a single parameter known as probability of miss. Based on this model, we analyze the voting principles and prove that the democratic voting principle has a merit to detect the joint support-set.
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