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dc.contributor.authorTran, The Truyen
dc.contributor.authorPhung, D.
dc.contributor.authorVenkatesh, S.
dc.identifier.citationTran, T.T. and Phung, D. and Venkatesh, S. 2011. Mixed-variate Restricted Boltzmann Machines, in Proceedings of the 3rd Asian Conference on Machine Learning Research, pp. 213-229, Nov 13-15 2011. Taoyuan, Taiwan: National Science Council.

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 classifier 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. © 2011 T. Tran, D. Phung & S. Venkatesh.

dc.titleMixed-variate restricted boltzmann machines
dc.typeConference Paper
dcterms.source.titleJournal of Machine Learning Research
dcterms.source.seriesJournal of Machine Learning Research

This open access article is distributed under the Creative Commons license

curtin.departmentMulti-Sensor Proc & Content Analysis Institute
curtin.accessStatusOpen access

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