2014 ParallelGibbsSamplingforHierarc

• (Cheng & Liu, 2014) ⇒ Dehua Cheng, and Yan Liu. (2014). “Parallel Gibbs Sampling for Hierarchical Dirichlet Processes via Gamma Processes Equivalence.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623708

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Parallel+Gibbs+Sampling+for+Hierarchical+Dirichlet+Processes+via+Gamma+Processes+Equivalence
• http://dl.acm.org/citation.cfm?id=2623330.2623708&preflayout=flat#citedby

Quotes

Author Keywords

• [[Hierarchical dirichlet process; parallel inference; probabilistic algorithms; topic model

Abstract

The hierarchical Dirichlet process (HDP) is an intuitive and elegant technique to model data with latent groups. However, it has not been widely used for practical applications due to the high computational costs associated with inference. In this paper, we propose an effective parallel Gibbs sampling algorithm for HDP by exploring its connections with the gamma-gamma-Poisson process. Specifically, we develop a novel framework that combines bootstrap and Reversible Jump MCMC algorithm to enable parallel variable updates. We also provide theoretical convergence analysis based on Gibbs sampling with asynchronous variable updates. Experiment results on both synthetic datasets and two large-scale text collections show that our algorithm can achieve considerable speedup as well as better inference accuracy for HDP compared with existing parallel sampling algorithms.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ParallelGibbsSamplingforHierarc	Yan Liu Dehua Cheng			Parallel Gibbs Sampling for Hierarchical Dirichlet Processes via Gamma Processes Equivalence				10.1145/2623330.2623708		2014