By the same authors

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From the same journal

Structural network inference from time-series data using a generative model and transfer entropy

Research output: Contribution to journalArticle

Published copy (DOI)

Author(s)

  • Zhihong Zhang
  • Genzhou Zhang
  • Zhonghao Zhang
  • Guo Chen
  • Yangbin Zeng
  • Beizhan Wang
  • Edwin R. Hancock

Department/unit(s)

Publication details

JournalPattern Recognition Letters
DateAccepted/In press - 24 May 2019
DateE-pub ahead of print (current) - 25 May 2019
Early online date25/05/19
Original languageEnglish

Abstract

In this paper we develop a novel framework for inferring a generative model of network structure representing the causal relations between data for a set of objects characterized in terms of time series. To do this we make use of transfer entropy as a means of inferring directed information transfer between the time-series data. Transfer entropy allows us to infer directed edges representing the causal relations between pairs of time series, and has thus been used to infer directed graph representations of causal networks for time-series data. We use the expectation maximization algorithm to learn a generative model which captures variations in the causal network over time. We conduct experiments on fMRI brain connectivity data for subjects in different stages of the development of Alzheimer’s disease (AD). Here we use the technique to learn class exemplars for different stages in the development of the disease, together with a normal control class, and demonstrate its utility in both graph multi-class and binary classifications. These experiments are showing the effectiveness of our proposed framework when the amounts of training data are relatively small.

Bibliographical note

© 2019 Elsevier B.V. All rights reserved. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy.

    Research areas

  • Transfer entropy, supergraph, Time series, Network inference, Expectation maximization algorithm

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