A stochastic variance-reduced coordinate descent algorithm for learning sparse Bayesian network from discrete high-dimensional data

This paper addresses the problem of learning a sparse structure Bayesian network from high-dimensional discrete data. Compared to continuous Bayesian networks, learning a discrete Bayesian network is a challenging problem due to the large parameter space. Although many approaches have been developed...

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Veröffentlicht in:International journal of machine learning and cybernetics Jg. 14; H. 3; S. 947 - 958
Hauptverfasser: Shajoonnezhad, Nazanin, Nikanjam, Amin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Bayesian analysis
Complex Systems
Computational Intelligence
Control
Engineering
Fines & penalties
Machine learning
Mechatronics
Networks
Optimization
Optimization algorithms
Original Article
Pattern Recognition
Probability distribution
Random variables
Robotics
Sparsity
Synthetic data
Systems Biology
Sparse structure learning
Bayesian networks
Stochastic gradient descent
Constrained optimization
ISSN:1868-8071, 1868-808X
Online-Zugang:Volltext
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Zusammenfassung:This paper addresses the problem of learning a sparse structure Bayesian network from high-dimensional discrete data. Compared to continuous Bayesian networks, learning a discrete Bayesian network is a challenging problem due to the large parameter space. Although many approaches have been developed for learning continuous Bayesian networks, few approaches have been proposed for the discrete ones. In this paper, we address learning Bayesian networks as an optimization problem and propose a score function which guarantees the learnt structure to be a sparse directed acyclic graph. Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function. Specifically, we use a variance reducing method in our optimization algorithm to make the algorithm work efficiently for high-dimensional data. The proposed approach is applied to synthetic data from well-known benchmark networks. The quality, scalability, and robustness of the constructed network are measured. Compared to some competitive approaches, the results reveal that our algorithm outperforms some of the well-known proposed methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-022-01674-9