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

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.