@inproceedings{oai:nagasaki-u.repo.nii.ac.jp:00017017,
 author = {Masada, Tomonari and Fukagawa, Daiji and Takasu, Atsuhiro and Hamada, Tsuyoshi and Shibata, Yuichiro and Oguri, Kiyoshi},
 book = {Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09},
 month = {},
 note = {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 sampling and evaluate our proposal by link detection task of Topic Detection and Tracking., Proceeding of the 18th ACM conference : Hong Kong, China, 2009.11.02-2009.11.06, Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09, pp1831-1834; 2009},
 pages = {1831--1834},
 publisher = {ACM Press},
 title = {Dynamic hyperparameter optimization for bayesian topical trend analysis},
 year = {2009}
}