Causal Network Inference Via Group Sparse Regularization.
Causal Network Inference Via Group Sparse Regularization.
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DOI：
10.1109/tsp.2011.2129515
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发表时间：
2011-06-11
期刊：
影响因子：
--
通讯作者：
Nowak R
中科院分区：
文献类型：
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作者：
Bolstad A;Van Veen BD;Nowak R
本文讨论了由多元自回归（MAR）过程建模的稀疏因果网络的推断问题。条件下得出的组套索（gLasso）程序一致估计稀疏网络结构。关键条件涉及“虚假连接分数”。特别是，我们表明，一致的恢复是可能的，即使当网络的观察数远远小于描述网络的参数的数量，只要ε < 1。假连接分数也被证明是一个有用的度量恢复非渐近制度。这些条件表明，修改后的gLasso程序往往会提高错误连接分数，并减少因果关系影响方向逆转的机会。计算实验和基于真实的神经网络的皮层电图（ECoG）仿真研究验证了该方法的有效性。
This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a “false connection score” ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach.