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An online gibbs sampler algorithm for hierarchical dirichlet processes prior SCOPUS

Title
An online gibbs sampler algorithm for hierarchical dirichlet processes prior
Authors
Kim, YCHAE, MINWOOJeong, K.Kang, B.Chung, H.
Date Issued
2016-09
Publisher
Springer Verlag
Abstract
The hierarchical Dirichlet processes (HDP) is a Bayesian nonparametric model that provides a flexible mixed-membership to documents. In this paper, we develop a novel mini-batch online Gibbs sampler algorithm for the HDP which can be easily applied to massive and streaming data. For this purpose, a new prior process so called the generalized hierarchical Dirichlet processes (gHDP) is proposed. The gHDP is an extension of the standard HDP where some prespecified topics can be included in the top-level Dirichlet process. By analyzing various datasets, we show that the proposed mini-batch online Gibbs sampler algorithm performs significantly better than the online variational algorithm for the HDP. © Springer International Publishing AG 2016.
URI
https://oasis.postech.ac.kr/handle/2014.oak/99025
DOI
10.1007/978-3-319-46128-1_32
ISSN
0302-9743
Article Type
Article
Citation
Lecture Notes in Computer Science, vol. 9851, page. 509 - 523, 2016-09
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