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An extension to the parsimonious topic modeling

Author
Chen, Yezhou
Published
[University Park, Pennsylvania] : Pennsylvania State University, 2015.
Physical Description
1 electronic document
Additional Creators
Miller, David J.
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Graduate Program
Restrictions on Access
Open Access.
Summary
In this thesis we develop a new model for estimating topics based on parsimonious topic modeland Latent Dirichlet Allocation. In parsimonious models, each word has a topic shared occurringprobability or a topic specific occurring probability for each topic and this is controlled by a switch.In our model, we use one more switch set to identify the mentioned switch subset(all switches forone word in all topics) by one of three cases: the word has a topic shared occurring probabilityfor all topics, the word has a topic specific occurring probability for all topics, the word has atopic shared occurring probability for some topics and a topic specific occurring probability forsome topics. We use a generalized Expectation-Maximization algorithm as a learning algorithmto optimize the parameters and minimize the objective function. Numerical results are presentedto examine the performance of such a model.
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Dissertation Note
M.S. Pennsylvania State University 2015.
Reproduction Note
Library holds archival microfiches negative and service copy. 1 fiche. (Micrographics International, 2016)
Technical Details
The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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