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

Journal Article


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. © 2014 Springer International Publishing.

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. Lecture Notes in Computer Science, 8675 321-328.

Scopus Eid


  • 2-s2.0-84906975882

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3246

Number Of Pages


  • 7

Start Page


  • 321

End Page


  • 328

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. © 2014 Springer International Publishing.

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. Lecture Notes in Computer Science, 8675 321-328.

Scopus Eid


  • 2-s2.0-84906975882

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3246

Number Of Pages


  • 7

Start Page


  • 321

End Page


  • 328

Volume


  • 8675