Ihou, Koffi Eddy and Bouguila, Nizar ORCID: https://orcid.org/0000000172247940 (2018) VariationalBased Latent Generalized Dirichlet Allocation Model in the Collapsed Space and Applications. Neurocomputing . ISSN 09252312 (In Press)
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Official URL: http://dx.doi.org/10.1016/j.neucom.2018.12.046
Abstract
In topic modeling framework, many Dirichletbased models performances have been hindered by the limitations of the conjugate prior. It led to models with more flexible priors, such as the generalized Dirichlet distribution, that tend to capture semantic relationships between topics (topic correlation). Now these extensions also suffer from incomplete generative processes that complicate performances in traditional inferences such as VB (Variational Bayes) and CGS (Collaspsed Gibbs Sampling). As a result, the new approach, the CVBLGDA (Collapsed Variational Bayesian inference for the Latent Generalized Dirichlet Allocation) presents a scheme that integrates a complete generative process to a robust inference technique for topic correlation and codebook analysis. Its performance in image classification, facial expression recognition, 3D objects categorization, and action recognition in videos shows its merits.
Divisions:  Concordia University > Gina Cody School of Engineering and Computer Science > Concordia Institute for Information Systems Engineering 

Item Type:  Article 
Refereed:  Yes 
Authors:  Ihou, Koffi Eddy and Bouguila, Nizar 
Journal or Publication:  Neurocomputing 
Date:  30 December 2018 
Funders: 

Digital Object Identifier (DOI):  10.1016/j.neucom.2018.12.046 
Keywords:  Topic model, generalized Dirichlet, topic correlation, 3D objects, images, categorization, facial expression recognition, action recognition in videos. 
ID Code:  984846 
Deposited By:  Michael Biron 
Deposited On:  10 Jan 2019 14:39 
Last Modified:  08 Dec 2020 02:00 
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