DocumentCode :
3110684
Title :
Gaussian Bayesian network structure learning strategies based on canonical correlation analysis
Author :
Li, Shuzhi ; Xu, Guanghua ; Feng, Yongbao
Author_Institution :
Sch. of Mech. Eng., Xi´´an Jiaotong Univ., Xi´´an, China
fYear :
2012
fDate :
5-8 Aug. 2012
Firstpage :
156
Lastpage :
161
Abstract :
In order to solve the problem of low efficiency and low reliability of Gaussian Bayesian network structure learning methods, this paper proposes a new Gaussian Bayesian network structure learning algorithm from data based on the canonical correlation analysis. Firstly, by canonical correlation analysis of the son node and the candidate parent nodes, the correlation coefficients and correlation variables are given. Because the correlation coefficient indicates the association strength of family structure, we use correlation coefficient as measures of the family structure. Secondly, a new algorithm to establish parent nodes based on correlation variables is introduced. According to the correlation vectors to calculate the contribution value of candidate parent nodes, the contribution value is used to evaluate the association strength of parent node to son node. These nodes with the bigger contribution value are considered as the father nodes. Finally, the Bayesian network structure learning strategies is given based on canonical correlation analysis. The experimental results on the simulation standard data sets show that the new algorithm is effective and reliable.
Keywords :
Gaussian processes; belief networks; learning (artificial intelligence); vectors; Gaussian Bayesian network structure learning algorithm; association strength; candidate parent node; canonical correlation analysis; correlation coefficient; correlation variables; correlation vectors; family structure; son node; Algorithm design and analysis; Bayesian methods; Correlation; Covariance matrix; Probability distribution; Vectors; Gaussian Bayesian networks; canonical correlation analysis; structure learning strategies;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechatronics and Automation (ICMA), 2012 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4673-1275-2
Type :
conf
DOI :
10.1109/ICMA.2012.6282824
Filename :
6282824
Link To Document :
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