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    Tensor-variate restricted boltzmann machines

    Access Status
    Fulltext not available
    Authors
    Nguyen, T.
    Tran, The Truyen
    Phung, D.
    Venkatesh, S.
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Nguyen, T. and Tran, T.T. and Phung, D. and Venkatesh, S. 2015. Tensor-variate restricted boltzmann machines, in Proceedings of the National Conference on Artificial Intelligence, pp. 2887-2893.
    Source Title
    Proceedings of the National Conference on Artificial Intelligence
    ISBN
    9781577357025
    School
    Multi-Sensor Proc & Content Analysis Institute
    URI
    http://hdl.handle.net/20.500.11937/33530
    Collection
    • Curtin Research Publications
    Abstract

    Restricted Boltzmann Machines (RBMs) are an important class of latent variable models for representing vector data. An under-explored area is multimode data, where each data point is a matrix or a tensor. Standard RBMs applying to such data would require vectorizing matrices and tensors, thus resulting in unnecessarily high dimensionality and at the same time, destroying the inherent higher-order interaction structures. This paper introduces Tensor-variate Restricted Boltzmann Machines (TvRBMs) which generalize RBMs to capture the multiplicative interaction between data modes and the latent variables. TvRBMs are highly compact in that the number of free parameters grows only linear with the number of modes. We demonstrate the capacity of TvRBMs on three real-world applications: handwritten digit classification, face recognition and EEG-based alcoholic diagnosis. The learnt features of the model are more discriminative than the rivals, resulting in better classification performance.

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