Topology identification of undirected consensus networks via sparse inverse covariance estimation
Topology identification of undirected consensus networks via sparse inverse covariance estimation
复制标题
通过稀疏逆协方差估计进行无向共识网络的拓扑识别
DOI：
10.1109/cdc.2016.7798973
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. Jovanović
中科院分区：
文献类型：
--
作者：
Sepideh Hassan;Neil K. Dhingra;M. Jovanović
研究了利用网络状态样本协方差矩阵识别稀疏交互拓扑的问题。具体来说，我们假设统计数据是由一个随机强制的无向一阶共识网络与未知的拓扑结构。我们提出了一种使用正则化高斯最大似然框架识别拓扑结构的方法，其中引入了正则化器作为诱导稀疏网络拓扑结构的手段。该算法采用序列二次近似，其中牛顿的方向是使用坐标下降法。我们提供了几个例子来证明良好的实际性能的方法。
We study the problem of identifying sparse interaction topology using sample covariance matrix of the state of the network. Specifically, we assume that the statistics are generated by a stochastically-forced undirected first-order consensus network with unknown topology. We propose a method for identifying the topology using a regularized Gaussian maximum likelihood framework where the ℓ1 regularizer is introduced as a means for inducing sparse network topology. The proposed algorithm employs a sequential quadratic approximation in which the Newton's direction is obtained using coordinate descent method. We provide several examples to demonstrate good practical performance of the method.
影响因子：
2.1
作者：
Friedman, Jerome;Hastie, Trevor;Tibshirani, Robert
通讯作者：
Tibshirani, Robert