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dc.contributor.authorTran, Truyen
dc.contributor.authorPhung, D.
dc.contributor.authorVenkatesh, S.
dc.contributor.editorC H Roi and Wray Buntine
dc.date.accessioned2017-01-30T10:40:05Z
dc.date.available2017-01-30T10:40:05Z
dc.date.created2013-03-07T20:00:37Z
dc.date.issued2012
dc.identifier.citationTran, Truyen and Phung, Dinh and Venkatesh, Svetha. 2012. Cumulative restricted Boltzmann machines for ordinal matrix data analysis, in Hoi, C.H. and Buntine, W. (ed), Proceedings of the 4th Asian conference on machine learning (ACML), Nov 4-6 2012, pp. 411-426. Singapore: JMLR.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/4560
dc.description.abstract

Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires,preferences etc. This paper investigates modelling of ordinal data with Gaussian restrictedBoltzmann machines (RBMs). In particular, we present the model architecture, learningand inference procedures for both vector-variate and matrix-variate ordinal data. We showthat our model is able to capture latent opinion prole of citizens around the world, andis competitive against state-of-art collaborative ltering techniques on large-scale publicdatasets. The model thus has the potential to extend application of RBMs to diversedomains such as recommendation systems, product reviews and expert assessments

dc.publisherJMLR
dc.relation.urihttp://jmlr.csail.mit.edu/proceedings/
dc.subjectordinal analysis
dc.subjectmatrix data
dc.subjectCumulative restricted Boltzmann machine
dc.titleCumulative restricted Boltzmann machines for ordinal matrix data analysis
dc.typeConference Paper
dcterms.source.startPage411
dcterms.source.endPage426
dcterms.source.title4th Asian Conference on Machine Learning
dcterms.source.seriesProc. of 4th Asian Conference on Machine Learning
dcterms.source.conferenceACML12
dcterms.source.conference-start-dateNov 4 2012
dcterms.source.conferencelocationSingapore
dcterms.source.placeSingapore
curtin.note

First published in the Journal of Machine Learning Research 2012.

curtin.department
curtin.accessStatusOpen access


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