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    Thurstonian Boltzmann machines: Learning from multiple inequalities

    Access Status
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    Authors
    Tran, The Truyen
    Phung, D.
    Venkatesh, S.
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Tran, T.T. and Phung, D. and Venkatesh, S. 2013. Thurstonian Boltzmann machines: Learning from multiple inequalities, pp. 705-713: International Machine Learning Society (IMLS).
    Source Title
    30th International Conference on Machine Learning, ICML 2013
    School
    Multi-Sensor Proc & Content Analysis Institute
    URI
    http://hdl.handle.net/20.500.11937/7956
    Collection
    • Curtin Research Publications
    Abstract

    We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables, and in the observation that each realisation of a discrete type imposes certain inequalities on those variables. Thus learning and inference in TBM reduce to making sense of a set of inequalities. Our proposed TBM naturally supports the following types: Gaussian, intervals, censored, binary, categorical, muticategorical, ordinal, (in)-complete rank with and without ties. We demonstrate the versatility and capacity of the proposed model on three applications of very different natures; namely handwritten digit recognition, collaborative filtering and complex social survey analysis. Copyright 2013 by the author(s).

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