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2020 Differential network inference via the fused D-trace loss with cross variables
Yichong Wu, Tiejun Li, Xiaoping Liu, Luonan Chen
Electron. J. Statist. 14(1): 1269-1301 (2020). DOI: 10.1214/20-EJS1691


Detecting the change of biological interaction networks is of great importance in biological and medical research. We proposed a simple loss function, named as CrossFDTL, to identify the network change or differential network by estimating the difference between two precision matrices under Gaussian assumption. The CrossFDTL is a natural fusion of the D-trace loss for the considered two networks by imposing the $\ell _{1}$ penalty to the differential matrix to ensure sparsity. The key point of our method is to utilize the cross variables, which correspond to the sum and difference of two precision matrices instead of using their original forms. Moreover, we developed an efficient minimization algorithm for the proposed loss function and further rigorously proved its convergence. Numerical results showed that our method outperforms the existing methods in both accuracy and convergence speed for the simulated and real data.


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Yichong Wu. Tiejun Li. Xiaoping Liu. Luonan Chen. "Differential network inference via the fused D-trace loss with cross variables." Electron. J. Statist. 14 (1) 1269 - 1301, 2020.


Received: 1 July 2019; Published: 2020
First available in Project Euclid: 25 March 2020

zbMATH: 07200229
MathSciNet: MR4079458
Digital Object Identifier: 10.1214/20-EJS1691

Primary: 62P10 , 65K10 , 92B15

Keywords: Coordinate descent , cross variables , Differential network , fused D-trace loss , Gaussian assumption


Vol.14 • No. 1 • 2020
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