A fast iterative algorithm for high-dimensional differential network
A fast iterative algorithm for high-dimensional differential network
复制标题
一种高维差分网络快速迭代算法
DOI：
10.1007/s00180-019-00915-w
复制
发表时间：
2020-03-01
影响因子：
1.3
通讯作者：
Wang, Cheng
中科院分区：
文献类型：
--
作者：
Tang, Zhou;Yu, Zhangsheng;Wang, Cheng
差分网络是捕获两个样本情况下条件相关性变化的重要工具。本文介绍了一种快速迭代算法来恢复高维数据的差分网络。本算法的计算复杂度在样本量和参数数上呈线性关系，与计算两个样本协方差矩阵的阶数相同，是最优的。该方法适用于小样本量的高维数据。在仿真和真实数据集上的实验表明，该算法优于其他现有方法。
A differential network is an important tool for capturing the changes in conditional correlations under two sample cases. In this paper, we introduce a fast iterative algorithm to recover the differential network for high-dimensional data. The computational complexity of our algorithm is linear in the sample size and the number of parameters, which is optimal in that it is of the same order as computing two sample covariance matrices. The proposed method is appealing for high-dimensional data with a small sample size. The experiments on simulated and real datasets show that the proposed algorithm outperforms other existing methods.