Sparse inverse covariance estimation with the graphical lasso
Sparse inverse covariance estimation with the graphical lasso
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DOI：
10.1093/biostatistics/kxm045
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发表时间：
2008-07-01
期刊：
影响因子：
2.1
通讯作者：
Tibshirani, Robert
中科院分区：
文献类型：
--
作者：
Friedman, Jerome;Hastie, Trevor;Tibshirani, Robert
我们考虑的问题估计稀疏图的套索惩罚施加到逆协方差矩阵。使用坐标下降过程的套索，我们开发了一个简单的算法图形套索，这是非常快的：它解决了1000节点的问题（类似于500000参数）在最多一分钟，是30-4000倍的速度比竞争的方法。它还提供了精确问题和Meinshausen和Buhlmann（2006）提出的近似之间的概念联系。我们说明了一些细胞信号数据的蛋白质组学的方法。
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm-the graphical lasso-that is remarkably fast: It solves a 1000-node problem (similar to 500000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Buhlmann (2006). We illustrate the method on some cell-signaling data from proteomics.