A fast iterative algorithm for high-dimensional differential network

Zhou Tang, Zhangsheng Yu, Cheng Wang

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)95-109
Number of pages15
JournalComputational Statistics
Volume35
Issue number1
DOIs
StatePublished - Mar 1 2020

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Keywords

  • ADMM
  • Differential network
  • Gaussian graphical model
  • High-dimensional data
  • Precision matrix

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Computational Mathematics

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