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Dependent hierarchical normalized random measures for dynamic topic modeling

Chen, Changyou; Ding, Nan; Buntine, Wray

Description

We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power law properties, unlike Dirichlet processes used previously in dynamic topic modeling. Inference for the model includes adapting a recently developed slice sampler to directly...[Show more]

dc.contributor.authorChen, Changyou
dc.contributor.authorDing, Nan
dc.contributor.authorBuntine, Wray
dc.coverage.spatialEdinburgh UK
dc.date.accessioned2015-12-10T23:32:24Z
dc.date.createdJune 26-July 1 2012
dc.identifier.isbn9781450312851
dc.identifier.urihttp://hdl.handle.net/1885/68827
dc.description.abstractWe develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power law properties, unlike Dirichlet processes used previously in dynamic topic modeling. Inference for the model includes adapting a recently developed slice sampler to directly manipulate the underlying Poisson process. Experiments performed on news, blogs, academic and Twitter collections demonstrate the technique gives superior perplexity over a number of previous models.
dc.publisherConference Organising Committee
dc.relation.ispartofseriesInternational Conference on Machine Learning (ICML 2012)
dc.sourceProceedings of the 29th International Conference on Machine Learning, ICML 2012
dc.subjectKeywords: Dirichlet process; Gamma process; Poisson process; Random measures; Learning systems; Poisson distribution
dc.titleDependent hierarchical normalized random measures for dynamic topic modeling
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2012
local.identifier.absfor080302 - Computer System Architecture
local.identifier.ariespublicationf5625xPUB1840
local.type.statusPublished Version
local.contributor.affiliationChen, Changyou, College of Engineering and Computer Science, ANU
local.contributor.affiliationDing, Nan, Purdue University
local.contributor.affiliationBuntine, Wray, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage895
local.bibliographicCitation.lastpage902
dc.date.updated2016-02-24T08:51:12Z
local.identifier.scopusID2-s2.0-84867136246
CollectionsANU Research Publications

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