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Max-margin based learning for discriminative Bayesian network from neuroimaging data

Conference Paper


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 maxmargin

    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-theart

    works in the discriminative power of SGBNs.

Publication Date


  • 2014

Citation


  • Zhou, L., Wang, L., Liu, L., Ogunbona, P. & Shen, D. (2014). Max-margin based learning for discriminative Bayesian network from neuroimaging data. In P. Golland, N. Hata, C. Barillot, J. Hornegger & R. J. Howe (Eds.), The 17th International Conference on Medical Image Computing and ComputerAssisted Intervention (MICCAI 2014) (pp. 321-328). Switzerland: Springer International Publishing.

Start Page


  • 321

End Page


  • 328

Place Of Publication


  • Switzerland

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 maxmargin

    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-theart

    works in the discriminative power of SGBNs.

Publication Date


  • 2014

Citation


  • Zhou, L., Wang, L., Liu, L., Ogunbona, P. & Shen, D. (2014). Max-margin based learning for discriminative Bayesian network from neuroimaging data. In P. Golland, N. Hata, C. Barillot, J. Hornegger & R. J. Howe (Eds.), The 17th International Conference on Medical Image Computing and ComputerAssisted Intervention (MICCAI 2014) (pp. 321-328). Switzerland: Springer International Publishing.

Start Page


  • 321

End Page


  • 328

Place Of Publication


  • Switzerland