A Simple Stochastic Gradient Variational Bayes for the Correlated Topic Model

摘要：

　　This paper proposes a new inference for the correlated topic model (CTM) 3.CTM is an extension of LDA 4 for modeling correlations among latent topics.The proposed inference is an instance of the stochastic gradient variational Bayes (SGVB) 7,8.By constructing the inference network with the diagonal logistic normal distribution,we achieve a simple inference.Especially,there is no need to invert the covariance matrix explicitly.We performed a comparison with LDA in terms of predictive perplexity.The two inferences for LDA are considered: the collapsed Gibbs sampling (CGS) 5 and the collapsed variational Bayes with a zero-order Taylor expansion approximation (CVB0) 1.While CVB0 for LDA gave the best result,the proposed inference achieved the perplexities comparable with those of CGS for LDA.

作者: Tomonari Masada Atsuhiro Takasu

作者单位: Nagasaki University,1-14 Bunkyo-machi,Nagasaki,Japan National Institute of Informatics,2-1-2 Hitotsubashi,Chiyoda-ku,Tokyo,Japan

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 424-428

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）