Proceedings of the International Conference on Computer Information Systems and Industrial Applications

Bayesian Parameter Estimation in LDA

Authors
Z.Y Liu, Y. Wang, W.P Wang, Z.Z Ji, W.Y Lu
Corresponding Author
Z.Y Liu
Available Online June 2015.
DOI
10.2991/cisia-15.2015.225How to use a DOI?
Keywords
bayesian; parameter estimation; LDA; topic model
Abstract

Latent Dirichlet Allocation (LDA) probabilistic topic model is widely used in text mining, natural language processing and so on. But LDA’s mathematical theory is particularly complex, thus it is very difficult to understand LDA for a novice. In order to more quickly and easily learn LDA, and further promote its application, this paper will deeply analyze LDA from the perspective of Bayesian parameter estimation. At first we explain the advantage of Bayesian parameter estimation by an instance, and then introduce a simple Bayesian Unigram model. Next based on the simple Bayesian Unigram model and PLSA model, a full Bayesian probabilistic topic model—LDA is presented.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the International Conference on Computer Information Systems and Industrial Applications
Series
Advances in Computer Science Research
Publication Date
June 2015
ISBN
978-94-62520-72-1
ISSN
2352-538X
DOI
10.2991/cisia-15.2015.225How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Z.Y Liu
AU  - Y. Wang
AU  - W.P Wang
AU  - Z.Z Ji
AU  - W.Y Lu
PY  - 2015/06
DA  - 2015/06
TI  - Bayesian Parameter Estimation in LDA
BT  - Proceedings of the International Conference on Computer Information Systems and Industrial Applications
PB  - Atlantis Press
SP  - 837
EP  - 840
SN  - 2352-538X
UR  - https://doi.org/10.2991/cisia-15.2015.225
DO  - 10.2991/cisia-15.2015.225
ID  - Liu2015/06
ER  -