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A fast iterative algorithm for high-dimensional differential network

Author

Listed:
  • Zhou Tang

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Zhangsheng Yu

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Cheng Wang

    (Shanghai Jiao Tong University)

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.

Suggested Citation

  • Zhou Tang & Zhangsheng Yu & Cheng Wang, 2020. "A fast iterative algorithm for high-dimensional differential network," Computational Statistics, Springer, vol. 35(1), pages 95-109, March.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00915-w
    DOI: 10.1007/s00180-019-00915-w
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    References listed on IDEAS

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    Cited by:

    1. Jarod Smith & Mohammad Arashi & Andriëtte Bekker, 2022. "Empowering differential networks using Bayesian analysis," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-19, January.

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