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Type: Journal article
Title: Learning discriminative Bayesian networks from high-dimensional continuous neuroimaging data
Author: Zhou, L.
Wang, L.
Liu, L.
Ogunbona, P.
Shen, D.
Citation: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016; 38(11):2269-2283
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2016
ISSN: 0162-8828
Statement of
Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, and Dinggang Shen
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.
Keywords: Bayesian network; discriminative learning; Fisher kernel learning; max-margin; brain network
Rights: © 2015 IEEE
DOI: 10.1109/TPAMI.2015.2511754
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