Joint Association Graph Screening and Decomposition for Large-Scale Linear Dynamical Systems
Joint Association Graph Screening and Decomposition for Large-Scale Linear Dynamical Systems
复制标题
大规模线性动力系统的联合关联图筛选与分解
DOI：
10.1109/tsp.2014.2373315
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发表时间：
2014
影响因子：
5.4
通讯作者：
D. Wu
中科院分区：
文献类型：
--
作者：
Yiyuan She;Yuejia He;Shijie Li;D. Wu
本文研究了大规模动态网络，其中系统的当前状态是前一个状态的线性变换，并被多变量高斯噪声污染。例子包括股票市场，人类大脑和基因调控网络。我们引入一个转移矩阵来描述演化过程，它可以转化为一个有向的格兰杰转移图，并使用高斯噪声的浓度矩阵来捕捉节点之间的二阶关系，它可以转化为一个无向的条件依赖图.我们建议正则化这两个图联合在拓扑识别和动态估计。基于联合关联图（JAG）的概念，我们开发了一个联合图形筛选和估计（JGSE）框架，用于大数据中的高效网络学习。特别是，我们的方法可以预先确定和删除不必要的边缘的基础上的联合图形结构，被称为JAG筛选，并可以分解一个大的网络到较小的子网络在一个强大的方式，被称为JAG分解。JAG筛选和分解可以减少问题规模和搜索空间，以便在后期进行精细估计。仿真数据和实际应用的实验表明，该框架在大规模网络拓扑识别和动态估计中是有效的。
This paper studies large-scale dynamical networks where the current state of the system is a linear transformation of the previous state, contaminated by a multivariate Gaussian noise. Examples include stock markets, human brains, and gene regulatory networks. We introduce a transition matrix to describe the evolution, which can be translated to a directed Granger transition graph, and use the concentration matrix of the Gaussian noise to capture the second-order relations between nodes, which can be translated to an undirected conditional dependence graph. We propose regularizing the two graphs jointly in topology identification and dynamics estimation. Based on the notion of joint association graph (JAG), we develop a joint graphical screening and estimation (JGSE) framework for efficient network learning in big data. In particular, our method can predetermine and remove unnecessary edges based on the joint graphical structure, referred to as JAG screening, and can decompose a large network into smaller subnetworks in a robust manner, referred to as JAG decomposition. JAG screening and decomposition can reduce the problem size and search space for fine estimation at a later stage. Experiments on both synthetic data and real-world applications show the effectiveness of the proposed framework in large-scale network topology identification and dynamics estimation.
影响因子：
2.7
作者：
Guo, Jian;Levina, Elizaveta;Zhu, Ji
通讯作者：
Zhu, Ji
DOI：
10.1214/11-aoas494
发表时间：
2011-12
期刊：
The annals of applied statistics
影响因子：
--
作者：
Yin J;Li H
通讯作者：
Li H