Learning hierarchical bayesian networks for large-scale data analysis

Authors:
Kyu-Baek Hwang;Byoung-Hee Kim;Byoung-Tak Zhang
Affiliations:
School of Computing, Soongsil University, Seoul, Korea;School of Computer Science and Engineering, Seoul National University, Seoul, Korea;School of Computer Science and Engineering, Seoul National University, Seoul, Korea
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 5
Cited 4

Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics

IEEE Transactions on Knowledge and Data Engineering
Construction of Large-Scale Bayesian Networks by Local to Global Search

PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Recognition of two-person interactions using a hierarchical Bayesian network

IWVS '03 First ACM SIGMM international workshop on Video surveillance
Tractable learning of large Bayes net structures from sparse data

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence

Landmark detection from mobile life log using a modular Bayesian network model

Expert Systems with Applications: An International Journal
Review: learning bayesian networks: Approaches and issues

The Knowledge Engineering Review
A new learning structure heuristic of bayesian networks from data

MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
A survey on latent tree models and applications

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bayesian network learning is a useful tool for exploratory data analysis. However, applying Bayesian networks to the analysis of large-scale data, consisting of thousands of attributes, is not straightforward because of the heavy computational burden in learning and visualization. In this paper, we propose a novel method for large-scale data analysis based on hierarchical compression of information and constrained structural learning, i.e., hierarchical Bayesian networks (HBNs). The HBN can compactly visualize global probabilistic structure through a small number of hidden variables, approximately representing a large number of observed variables. An efficient learning algorithm for HBNs, which incrementally maximizes the lower bound of the likelihood function, is also suggested. The effectiveness of our method is demonstrated by the experiments on synthetic large-scale Bayesian networks and a real-life microarray dataset.