Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
This work addresses the need for more discriminative models in neuroimaging-based brain network analysis, though it is incremental as it builds on existing GBN methods.
The paper tackled the problem of Bayesian networks (BN) being generative and not discriminative, which can ignore subtle network changes across populations, by proposing two frameworks to improve discriminative power for Gaussian Bayesian networks (GBN) in neuroimaging data, resulting in strong performance in learning discriminative parameters while maintaining representation capacity.
Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.