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Issue No. 06 - June (2013 vol. 35)
ISSN: 0162-8828
pp: 1328-1342
Shuai Huang , Arizona State University, Tempe
Jing Li , Arizona State University, Tempe
Jieping Ye , Arizona State University, Tempe
Adam Fleisher , Banner Alzheimer's Institute, Phoenix
Kewei Chen , Banner Alzheimer's Institute, Phoenix
Teresa Wu , Arizona State University, Tempe
Eric Reiman , Banner Alzheimer's Institute, Phoenix
Structure learning of Bayesian Networks (BNs) is an important topic in machine learning. Driven by modern applications in genetics and brain sciences, accurate and efficient learning of large-scale BN structures from high-dimensional data becomes a challenging problem. To tackle this challenge, we propose a Sparse Bayesian Network (SBN) structure learning algorithm that employs a novel formulation involving one L1-norm penalty term to impose sparsity and another penalty term to ensure that the learned BN is a Directed Acyclic Graph (DAG)—a required property of BNs. Through both theoretical analysis and extensive experiments on 11 moderate and large benchmark networks with various sample sizes, we show that SBN leads to improved learning accuracy, scalability, and efficiency as compared with 10 existing popular BN learning algorithms. We apply SBN to a real-world application of brain connectivity modeling for Alzheimer's disease (AD) and reveal findings that could lead to advancements in AD research.
Algorithm design and analysis, Bayesian methods, Input variables, Machine learning, Accuracy, Brain models

S. Huang et al., "A Sparse Structure Learning Algorithm for Gaussian Bayesian Network Identification from High-Dimensional Data," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 6, pp. 1328-1342, 2013.
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