Representation method for a set of documents from the viewpoint of Bayesian statistics

Masayuki Goto*, Takashi Ishida, Shigeichi Hirasawa

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


In this paper, we consider the Bayesian approach for representation of a set of documents. In the field of representation of a set of documents, many previous models, such as the latent semantic analysis (LSA), the probabilistic latent semantic analysis (PLSA), the Semantic Aggregate Model (SAM), the Bayesian Latent Semantic Analysis (BLSA), and so on, were proposed. In this paper, we formulate the Bayes optimal solutions for estimation of parameters and selection of the dimension of the hidden latent class in these models and analyze it's asymptotic properties.

Original languageEnglish
Pages (from-to)4637-4642
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2003
Externally publishedYes
EventSystem Security and Assurance - Washington, DC, United States
Duration: 2003 Oct 52003 Oct 8


  • Automated document indexing
  • Bayesian statistics
  • Information retrieval
  • Probabilistic latent semantic indexing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture


Dive into the research topics of 'Representation method for a set of documents from the viewpoint of Bayesian statistics'. Together they form a unique fingerprint.

Cite this