Learning discriminative Bayesian networks from high-dimensional continuous neuroimaging data

Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.

Original languageEnglish
Article number7364252
Pages (from-to)2269-2283
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume38
Issue number11
DOIs
Publication statusPublished - 2016 Nov

Keywords

  • Bayesian network
  • Brain network
  • Discriminative learning
  • Fisher kernel learning
  • Max-margin

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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