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    Mixed-variate restricted boltzmann machines

    233294_233294.pdf (466.6Kb)
    Access Status
    Open access
    Authors
    Tran, The Truyen
    Phung, D.
    Venkatesh, S.
    Date
    2011
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Tran, 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.
    Source Title
    Journal of Machine Learning Research
    ISSN
    1532-4435
    School
    Multi-Sensor Proc & Content Analysis Institute
    Remarks

    This open access article is distributed under the Creative Commons license http://creativecommons.org/licenses/by-sa/3.0/

    URI
    http://hdl.handle.net/20.500.11937/7707
    Collection
    • Curtin Research Publications
    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 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.

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