Advertisement

Max-Margin Based Learning for Discriminative Bayesian Network from Neuroimaging Data

  • Luping Zhou
  • Lei Wang
  • Lingqiao Liu
  • Philip Ogunbona
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

Recently, neuroimaging data have been increasingly used to study the causal relationship among brain regions for the understanding and diagnosis of brain diseases. Recent work on sparse Gaussian Bayesian network (SGBN) has shown it as an efficient tool to learn large scale directional brain networks from neuroimaging data. In this paper, we propose a learning approach to constructing SGBNs that are both representative and discriminative for groups in comparison. A max-margin criterion built directly upon the SGBN models is proposed to effectively optimize the classification performance of the SGBNs. The proposed method shows significant improvements over the state-of-the-art works in the discriminative power of SGBNs.

References

  1. 1.
    Bressler, S., Menon, V.: Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 14(6), 227–290 (2010)CrossRefGoogle Scholar
  2. 2.
    Huang, S., Li, J., Ye, J., Fleisher, A., Chen, K., Wu, T., Reiman, E.: A sparse structure learning algorithm for gaussian bayesian network identification from high-dimensional data. IEEE TPAMI 35(6), 1328–1342 (2013)CrossRefGoogle Scholar
  3. 3.
    Li, X., Coyle, D., Maguire, L., Watson, D., McGinnity, T.: Gray matter concentration and effective connectivity changes in alzheimer’s disease: A longitudinal structural mri study. Neuroradiology 53(10), 733–748 (2011)CrossRefGoogle Scholar
  4. 4.
    Kim, J., Zhu, W., Chang, L., Bentler, P., Ernst, T.: Unified structural equation modeling approach for the analysis of multisubject, multivariate functional mri data. Human Brain Mapping 28, 85–93 (2007)CrossRefGoogle Scholar
  5. 5.
    Friston, K., Harrison, L., Penney, W.: Dynamic causal modeling. Neuroimage 19, 1273–1302 (2003)CrossRefGoogle Scholar
  6. 6.
    Zhou, L., Wang, L., Liu, L., Ogunbona, P., Shen, D.: Discriminative brain effective connectivity analysis for alzheimers disease: A kernel learning approach upon sparse gaussian bayesian network. In: CVPR, pp. 2243–2250 (2013)Google Scholar
  7. 7.
    Pernkopf, F., Bilmes, J.: Efficient heuristics for discriminative structure learning of bayesian network classifiers. J. Mach. Learn. Res. 11, 2323–2360 (2010)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Guo, Y., Wilkinson, D., Schuurmans, D.: Maximum margin bayesian networks. In: UAI, pp. 233–242. AUAI Press (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Luping Zhou
    • 1
  • Lei Wang
    • 1
  • Lingqiao Liu
    • 2
  • Philip Ogunbona
    • 1
  • Dinggang Shen
    • 3
  1. 1.University of WollongongAustralia
  2. 2.University of AdelaideAustralia
  3. 3.University of North Carolina at Chapel HillUSA

Personalised recommendations