Testing the Structure of a Gaussian Graphical Model With Reduced Transmissions in a Distributed Setting
Testing the Structure of a Gaussian Graphical Model With Reduced Transmissions in a Distributed Setting
复制标题
在分布式环境中测试具有减少传输的高斯图模型的结构
DOI：
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发表时间：
2019
影响因子：
5.4
通讯作者：
Jiangfan Zhang
中科院分区：
文献类型：
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作者：
Yicheng Chen;Rick S. Blum;Brian M. Sadler;Jiangfan Zhang
本文基于一组分布传感器的观测值，考虑高斯图模型（GGM）下协方差矩阵的检验。提出了有序传输以实现与最优集中式能量无约束方法相同的贝叶斯风险，但具有更少的传输和完全分布式的方法。在这种方法中，我们将贝叶斯最优检验统计量表示为局部检验统计量的总和，该统计量可以通过仅利用一个聚类中可用的观测值来计算。我们选择一个传感器作为簇头（CH）收集和总结的观察数据在每个集群和集群间的通信被假定为是廉价的。具有更多信息观测的CH首先将其数据传输到融合中心（FC）。通过在所有传输发生之前停止，可以在不损失性能的情况下保存传输。结果表明，这种排序方法可以保证一个下界的平均传输次数节省任何给定的GGM和下界可以接近约一半的集群数量时，在每个集群的备择假设下的协方差矩阵的最小特征值变得足够大。
Testing a covariance matrix following a Gaussian graphical model (GGM) is considered in this paper based on observations made at a set of distributed sensors grouped into clusters. Ordered transmissions are proposed to achieve the same Bayes risk as the optimum centralized energy unconstrained approach but with fewer transmissions and a completely distributed approach. In this approach, we represent the Bayes optimum test statistic as a sum of local test statistics which can be calculated by only utilizing the observations available at one cluster. We select one sensor to be the cluster head (CH) to collect and summarize the observed data in each cluster and intercluster communications are assumed to be inexpensive. The CHs with more informative observations transmit their data to the fusion center (FC) first. By halting before all transmissions have taken place, transmissions can be saved without performance loss. It is shown that this ordering approach can guarantee a lower bound on the average number of transmissions saved for any given GGM and the lower bound can approach approximately half the number of clusters when the minimum eigenvalue of the covariance matrix under the alternative hypothesis in each cluster becomes sufficiently large.