FANOK:线性时间的仿冒品

IF 1.9 Q1 MATHEMATICS, APPLIED
Armin Askari, Quentin Rebjock, A. d’Aspremont, L. Ghaoui
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引用次数: 2

摘要

我们描述了一系列有效实现高斯模型x仿制品的算法,以控制大规模特征选择问题的错误发现率。识别仿冒品的分布需要求解一个大规模的半定程序,为此我们推导了几种有效的方法。一个处理一般的协方差矩阵,其复杂度缩放为$O(p^3)$,其中$p$是环境维度,而另一个在协方差矩阵上假设秩$k$因子模型,以将复杂度降低到$O(pk^2)$。我们还推导了估算因子模型和样本仿制品协变量的有效方法,这些协变量的复杂度在维度上是线性的。我们在$p$高达$500,000$的问题上测试我们的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FANOK: Knockoffs in Linear Time
We describe a series of algorithms that efficiently implement Gaussian model-X knockoffs to control the false discovery rate on large scale feature selection problems. Identifying the knockoff distribution requires solving a large scale semidefinite program for which we derive several efficient methods. One handles generic covariance matrices, has a complexity scaling as $O(p^3)$ where $p$ is the ambient dimension, while another assumes a rank $k$ factor model on the covariance matrix to reduce this complexity bound to $O(pk^2)$. We also derive efficient procedures to both estimate factor models and sample knockoff covariates with complexity linear in the dimension. We test our methods on problems with $p$ as large as $500,000$.
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