Paper Info
Title
Representation method for a set of documents from the viewpoint of Bayesian statistics
Abstract
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.
Year
DOI
Venue
2003
10.1109/ICSMC.2003.1245715
Systems, Man and Cybernetics, 2003. IEEE International Conference
Keywords
Field
DocType
Bayes methods,belief networks,indexing,information retrieval,parameter estimation,semantic networks,Bayes optimal solutions,Bayesian latent semantic analysis,asymptotic properties,hidden latent class,parameter estimation,probabilistic latent semantic analysis,representation method,semantic aggregate model
Data mining,Latent Dirichlet allocation,Computer science,Latent class model,Semantic network,Document-term matrix,Probabilistic latent semantic analysis,Artificial intelligence,Bayesian statistics,Latent semantic analysis,Machine learning,Bayes' theorem
Conference
Volume
ISSN
ISBN
5
1062-922X
0-7803-7952-7
Citations 
PageRank 
References 
1
0.48
0
Authors
3
Name
Order
Citations
PageRank
Masayuki Goto172.51
Takashi Ishida2125.23
Shigeichi Hirasawa3322150.91