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Dynamic hyperparameter optimization for bayesian topical trend analysis
2009
Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09
This paper presents a new Bayesian topical trend analysis. We regard the parameters of topic Dirichlet priors in latent Dirichlet allocation as a function of document timestamps and optimize the parameters by a gradient-based algorithm. Since our method gives similar hyperparameters to the documents having similar timestamps, topic assignment in collapsed Gibbs sampling is affected by timestamp similarities. We compute TFIDF-based document similarities by using a result of collapsed Gibbs
doi:10.1145/1645953.1646242
dblp:conf/cikm/MasadaFTHSO09
fatcat:4zgmx3vfg5h5xe2xjokir5gdaq