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.