Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Contractive rectifier networks for nonlinear maximum margin classification

    Access Status
    Fulltext not available
    Authors
    An, Senjian
    Hayat, M.
    Khan, S.
    Bennamoun, M.
    Boussaid, F.
    Sohel, F.
    Date
    2015
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    An, S. and Hayat, M. and Khan, S. and Bennamoun, M. and Boussaid, F. and Sohel, F. 2015. Contractive rectifier networks for nonlinear maximum margin classification, pp. 2515-2523.
    Source Title
    Proceedings of the IEEE International Conference on Computer Vision
    DOI
    10.1109/ICCV.2015.289
    ISBN
    9781467383912
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/69913
    Collection
    • Curtin Research Publications
    Abstract

    © 2015 IEEE. To find the optimal nonlinear separating boundary with maximum margin in the input data space, this paper proposes Contractive Rectifier Networks (CRNs), wherein the hidden-layer transformations are restricted to be contraction mappings. The contractive constraints ensure that the achieved separating margin in the input space is larger than or equal to the separating margin in the output layer. The training of the proposed CRNs is formulated as a linear support vector machine (SVM) in the output layer, combined with two or more contractive hidden layers. Effective algorithms have been proposed to address the optimization challenges arising from contraction constraints. Experimental results on MNIST, CIFAR-10, CIFAR-100 and MIT-67 datasets demonstrate that the proposed contractive rectifier networks consistently outperform their conventional unconstrained rectifier network counterparts.

    Related items

    Showing items related by title, author, creator and subject.

    • How can deep rectifier networks achieve linear separability and preserve distances?
      An, Senjian; Boussaid, F.; Bennamoun, M. (2015)
      This paper investigates how hidden layers of deep rectifier networks are capable of transforming two or more pattern sets to be linearly separable while preserving the distances with a guaranteed degree, and proves the ...
    • Sign constrained rectifier networks with applications to pattern decompositions
      An, Senjian; Ke, Q.; Bennamoun, M.; Boussaid, F.; Sohel, F. (2015)
      © Springer International Publishing Switzerland 2015. In this paper we introduce sign constrained rectifier networks (SCRN), demonstrate their universal classification power and illustrate their applications to pattern ...
    • Investigation of activation functions in deep belief network
      Lau, M.; Lim, Hann (2017)
      © 2017 IEEE. Deep Belief Network (DBN) is made up of stacked Restricted Boltzmann Machine layers associated with global weight fine-tuning for pattern recognition. However, DBN suffers from vanishing gradient problem due ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.