Bayesian Parameter Estimation in LDA

被引：0

作者：

Liu, Z. Y. ^{[1
,2
]}

Wang, W. P. ^{[2
]}

Wang, Y. ^{[1
]}

Lu, W. Y. ^{[1
]}

Ji, Z. Z. ^{[3
]}

机构：

[1] Beijing Inst Technol, Sch Software, Beijing, Peoples R China

[2] Chinese Acad Sci, Inst Informat Engn, Beijing 100864, Peoples R China

[3] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL APPLICATIONS (CISIA 2015) | 2015年 / 18卷

关键词：

bayesian; parameter estimation; LDA; topic model;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：837 / 840

页数：4

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