In this paper,a nonparametric Bayesian graph topic model(GTM)based on hierarchical Dirichlet process(HDP)is proposed.The HDP makes the number of topics selected flexibly,which breaks the limitation that the number of ...In this paper,a nonparametric Bayesian graph topic model(GTM)based on hierarchical Dirichlet process(HDP)is proposed.The HDP makes the number of topics selected flexibly,which breaks the limitation that the number of topics need to be given in advance.Moreover,theGTMreleases the assumption of‘bag of words’and considers the graph structure of the text.The combination of HDP and GTM takes advantage of both which is named as HDP–GTM.The variational inference algorithm is used for the posterior inference and the convergence of the algorithm is analysed.We apply the proposed model in text categorisation,comparing to three related topic models,latent Dirichlet allocation(LDA),GTM and HDP.展开更多
基金supported by NSFC under grant No.71371074the 111 Project under No.B14019.
文摘In this paper,a nonparametric Bayesian graph topic model(GTM)based on hierarchical Dirichlet process(HDP)is proposed.The HDP makes the number of topics selected flexibly,which breaks the limitation that the number of topics need to be given in advance.Moreover,theGTMreleases the assumption of‘bag of words’and considers the graph structure of the text.The combination of HDP and GTM takes advantage of both which is named as HDP–GTM.The variational inference algorithm is used for the posterior inference and the convergence of the algorithm is analysed.We apply the proposed model in text categorisation,comparing to three related topic models,latent Dirichlet allocation(LDA),GTM and HDP.
