Show simple item record

dc.contributor.authorTran, Truyen
dc.contributor.authorPhung, Dinh
dc.contributor.authorVenkatesh, Svetha
dc.contributor.editorChun-Nan Hsu and Wee Sun Lee
dc.date.accessioned2017-01-30T13:45:27Z
dc.date.available2017-01-30T13:45:27Z
dc.date.created2012-03-01T20:00:55Z
dc.date.issued2011
dc.identifier.citationTran, Truyen and Phung, Dinh and Venkatesh, Svetha. 2011. Mixed-Variate Restricted Boltzmann Machines, in Hsu, Chun-Nan and Lee, Wee Sun (ed), JMLR Workshop and Conference Proceedings. Volume 20: Asian Conference on Machine Learning, Nov 13 2011, pp. 213-229. Taoyuan, Taiwan: JMLR
dc.identifier.urihttp://hdl.handle.net/20.500.11937/34741
dc.description.abstract

Modern datasets are becoming heterogeneous. To this end, we present in this paper Mixed- Variate Restricted Boltzmann Machines for simultaneously modelling variables of multiple types and modalities, including binary and continuous responses, categorical options, multicategorical choices, ordinal assessment and category-ranked preferences. Dependency among variables is modeled using latent binary variables, each of which can be interpreted as a particular hidden aspect of the data. The proposed model, similar to the standard RBMs, allows fast evaluation of the posterior for the latent variables. Hence, it is naturally suitable for many common tasks including, but not limited to, (a) as a pre-processing step to convert complex input data into a more convenient vectorial representation through the latent posteriors, thereby offering a dimensionality reduction capacity, (b) as a classier supporting binary, multiclass, multilabel, and label-ranking outputs, or a regression tool for continuous outputs and (c) as a data completion tool for multimodal and heterogeneous data. We evaluate the proposed model on a large-scale dataset using the world opinion survey results on three tasks: feature extraction and visualization, data completion and prediction.

dc.publisherJMLR
dc.relationhttp://jmlr.csail.mit.edu/proceedings/papers/v20/tran11/tran11.pdf
dc.titleMixed-Variate Restricted Boltzmann Machines
dc.typeConference Paper
dcterms.source.volume20
dcterms.source.startPage213
dcterms.source.endPage229
dcterms.source.titleMix-variate restricted boltzmann machines
dcterms.source.seriesMix-variate restricted boltzmann machines
dcterms.source.conference3rd Asian Conference on Machine Learning
dcterms.source.conference-start-dateNov 13 2011
dcterms.source.conferencelocationTaoyuan, Taiwan
dcterms.source.placeUnited States
curtin.departmentDepartment of Computing
curtin.accessStatusFulltext not available


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record