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Mixed-variate restricted Boltzmann machines

Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2011, Mixed-variate restricted Boltzmann machines, in ACML 2011 : Proceedings of the 3rd Asian Conference on Machine Learning, [JMLR], [Taoyuan, Taiwan], pp. 213-229.

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Title Mixed-variate restricted Boltzmann machines
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Asian Conference on Machine Learning (3rd : 2011 : Taoyuan, Taiwan)
Conference location Taoyuan, Taiwan
Conference dates 13-15 Nov. 2011
Title of proceedings ACML 2011 : Proceedings of the 3rd Asian Conference on Machine Learning
Editor(s) Hsu, Chun-Nan
Lee, Wee Sun
Publication date 2011
Conference series Asian Conference on Machine Learning
Start page 213
End page 229
Total pages 17
Publisher [JMLR]
Place of publication [Taoyuan, Taiwan]
Keyword(s) Boltzmann machines
datasets
latent binary variables
Summary 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 oering 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.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2011, The Authors
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044781

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.