Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data

TL;DR

This paper introduces two novel discriminative learning frameworks for Gaussian Bayesian networks to enhance their ability to detect subtle network changes in high-dimensional neuroimaging data, improving brain network analysis.

Contribution

It proposes two new frameworks for discriminative learning of Gaussian Bayesian networks, incorporating Fisher kernel and max-margin criteria, with a new DAG constraint ensuring graph validity.

Findings

Both frameworks outperform traditional methods in neuroimaging classification tasks.

The Fisher kernel approach effectively bridges generative and discriminative models.

The max-margin GBN approach explicitly optimizes classification performance.

Abstract

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.