Exact Topology Learning in a Network of Cyclostationary Processes
Exact Topology Learning in a Network of Cyclostationary Processes
复制标题
循环平稳过程网络中的精确拓扑学习
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Salapaka
中科院分区：
文献类型：
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作者：
Harish Doddi;Saurav Talukdar;Deepjyoti Deka;M. Salapaka
从时间序列数据，特别是周期平稳数据中学习网络的结构，在许多学科，如电网，生物学，金融中具有重要意义。本文提出了一种用于重建环平稳过程网络拓扑结构的算法。据我们所知，这是第一次在没有对潜在结构进行任何假设的情况下保证精确恢复的工作。该方法基于一种提升技术，该技术将循环平稳过程映射到向量广义平稳过程，并进一步利用上述过程的矩阵维纳滤波器的半确定性质。我们在一个电阻-电容网络上演示了所提出算法的性能，并给出了不同样本量下重建的准确性。
Learning the structure of a network from time-series data, in particular cyclostationary data, is of significant interest in many disciplines such as power grids, biology, finance. In this article, an algorithm is presented for reconstruction of the topology of a network of cyclostationary processes. To the best of our knowledge, this is the first work to guarantee exact recovery without any assumptions on the underlying structure. The method is based on a lifting technique by which cyclostationary processes are mapped to vector wide sense stationary processes and further on semi-definite properties of matrix Wiener filters for the said processes. We demonstrate the performance of the proposed algorithm on a Resistor-Capacitor network and present the accuracy of reconstruction for varying sample sizes.
影响因子：
6.8
作者：
Goncalves, Jorge;Warnick, Sean
通讯作者：
Warnick, Sean