Hidden Variable Discovering Algorithm of Bayesian Networks Based on Structural Decomposition and Factor Analysis
Hao Wang · 2012
Hidden variables are unobservable or virtual variables,and the hidden variables cannot be effectively disco-vered by directly using the learning methods of data driven.The structure analysis methods are used to find hidden variables.Bcause the number and location of hidden variables are difficult to be determined,a learning algorithm(S-FAHF) of hidden variables was presented based on structural decomposition and factor analysis.The S-FAHF algorithm obtains the variables sets(Cliques) by junction tree algorithm,and the variables in a set have stronger dependence relationships.Then,the factor analysis method is inducted to discriminate the number and location of hidden variables for cliques;finally,the BIC scoring function is used to test validity of hidden variables.The results of algorithm comparison and experiment show that S-FAHF algorithm can effectively determine the number of hidden variables and their location.