A generative semi-supervised model for multi-view learning when some views are label-free

Gaole Jin, Raviv Raich, David Jonathan Miller

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    11 Scopus citations

    Abstract

    We consider multi-view classification for the challenging scenario where, for some views, there are no labeled training examples. Several discriminative approaches have been recently proposed for special instances of this problem. Here, alternatively, we propose a generative semi-supervised mixture model across all views which, via marginalization, flexibly performs exact class inference, given any subset of available views. The proposed model is an extension of semi-supervised mixtures to a multi-view setting, as well as a semi-supervised extension of mixtures of factors analyzers (MFA)[1]. A novel EM algorithm with a computationally efficient E-step is derived for learning our multi-view model. Specialization of this formulation to the standard MFA problem also gives a reduced complexity E-step, compared to the original EM algorithm proposed for MFA. Our multi-view method is experimentally demonstrated on digit recognition using audio and lip video views, achieving competitive results with alternative, discriminative approaches.

    Original languageEnglish (US)
    Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
    Pages3302-3306
    Number of pages5
    DOIs
    StatePublished - Oct 18 2013
    Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
    Duration: May 26 2013May 31 2013

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ISSN (Print)1520-6149

    Other

    Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
    CountryCanada
    CityVancouver, BC
    Period5/26/135/31/13

    All Science Journal Classification (ASJC) codes

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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