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

Authors

Luping Zhou, University of WollongongFollow
Lei Wang, University of WollongongFollow
Lingqiao Liu, University of AdelaideFollow
Philip O. Ogunbona, University of WollongongFollow
Dinggang Shen, University of North Carolina

RIS ID

119688

Publication Details

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.

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.