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Sparse adaptive Dirichlet-multinomial-like processes

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dc.contributor.authorHutter, Marcus
dc.coverage.spatialPrinceton, United States of America
dc.date.accessioned2015-08-14T04:29:41Z
dc.date.available2015-08-14T04:29:41Z
dc.date.created12 June 2013 through 14 June 2013
dc.date.issued2013-06
dc.date.updated2018-11-29T08:20:32Z
dc.description.abstractOnline estimation and modelling of i.i.d. data for short sequences over large or complex ''alphabets'' is a ubiquitous (sub)problem in machine learning, information theory, data compression, statistical language processing, and document analysis. The Dirichlet-Multinomial distribution (also called Polya urn scheme) and extensions thereof are widely applied for online i.i.d. estimation. Good a-priori choices for the parameters in this regime are difficult to obtain though. I derive an optimal adaptive choice for the main parameter via tight, data-dependent redundancy bounds for a related model. The 1-line recommendation is to set the 'total mass' = 'precision' = 'concentration' parameter to m/2ln[(n+1)/m], where n is the (past) sample size and m the number of different symbols observed (so far). The resulting estimator is simple, online, fast, and experimental performance is superb.
dc.identifier.isbn1938-7228
dc.identifier.issn1532-4435en_AU
dc.identifier.urihttp://hdl.handle.net/1885/14721
dc.publisherJournal of Machine Learning Research
dc.relation.ispartofConference on Learning Theory: JMLR Workshop and Conference Proceedings, volume 30
dc.relation.ispartofseries26th Conference on Learning Theory, COLT 2013
dc.rights© 2013 M. Hutter. Author can archive publisher’s version/PDF. http://www.sherpa.ac.uk/romeo/issn/1532-4435/ as at 14/8/15
dc.sourceJournal of Machine Learning Research
dc.source.urihttp://www.jmlr.org/proceedings
dc.subjectsparse coding
dc.subjectadaptive parameters
dc.subjectDirichlet-Multinomial
dc.subjectPolya urn
dc.subjectdata-dependent redundancy bound
dc.subjectsmall/large alphabet
dc.subjectdata compression
dc.titleSparse adaptive Dirichlet-multinomial-like processes
dc.typeConference paper
local.bibliographicCitation.lastpage28en_AU
local.bibliographicCitation.startpage1en_AU
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National Universityen_AU
local.contributor.authoruidu4350841en_AU
local.identifier.absfor080104 - Computer Vision
local.identifier.absfor010405 - Statistical Theory
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB1166
local.identifier.scopusID2-s2.0-84898021490
local.type.statusPublished Versionen_AU

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