Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach
Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach
复制标题
使用贪婪坐标上升方法学习稀疏高斯马尔可夫网络
DOI：
10.1007/978-3-642-15939-8_13
复制
发表时间：
2010
期刊：
影响因子：
--
通讯作者：
I. Rish
中科院分区：
文献类型：
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作者：
K. Scheinberg;I. Rish
在本文中，我们介绍了一个简单而有效的贪婪算法，称为SINCO，稀疏逆协方差选择问题，这相当于学习一个稀疏高斯马尔可夫网络，并实证研究的结构恢复性能的算法。我们的方法是基于一个坐标上升的方法，自然保留了网络结构的稀疏性。我们表明，SINCO通常与glasso [7]和COVSEL [1]等常用方法相当，并且在各种情况下，在结构重建误差（特别是假阳性误差）和计算时间方面优于这些方法。此外，我们的方法具有易于并行化的优点。最后，我们证明了SINCO的贪婪性质允许通过仅将该方法应用于正则化参数λ的一个（足够小的）实例来再现正则化路径行为;因此，SINCO可以直接获得所需数量的网络链路，而无需调整λ参数。我们评估我们的方法经验上的各种模拟网络和现实生活中的数据，从生物和神经成像应用。
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Gaussian Markov Network, and empirically investigate the structure-recovery properties of the algorithm. Our approach is based on a coordinate ascent method which naturally preserves the sparsity of the network structure. We show that SINCO is often comparable to, and, in various cases, outperforms commonly used approaches such as glasso [7] and COVSEL [1], in terms of both structure-reconstruction error (particularly, false positive error) and computational time. Moreover, our method has the advantage of being easily parallelizable. Finally, we show that SINCO's greedy nature allows reproduction of the regularization path behavior by applying the method to one (sufficiently small) instance of the regularization parameter λ only; thus, SINCO can obtain a desired number of network links directly, without having to tune the λ parameter. We evaluate our method empirically on various simulated networks and real-life data from biological and neuroimaging applications.