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Unsupervised Multi-modal Learning

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Advances in Artificial Intelligence (Canadian AI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9091))

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Abstract

We present an unsupervised deep belief network that can learn from multiple channels and is capable of dealing with missing information. The network learns transferable features to accommodate the addition of a new channel using a combination of unsupervised learning and a simple back-fitting.

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Author information

Authors and Affiliations

  1. Jodrey School of Computer Science, Acadia University, Wolfville, NS, B4P 2R6, Canada

    Mohammed Shameer Iqbal

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  1. Mohammed Shameer Iqbal
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Correspondence to Mohammed Shameer Iqbal .

Editor information

Editors and Affiliations

  1. University of Alberta, Edmonton, Canada

    Denilson Barbosa

  2. Dalhousie University, Halifax, Canada

    Evangelos Milios

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Iqbal, M.S. (2015). Unsupervised Multi-modal Learning. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_32

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  • DOI: https://doi.org/10.1007/978-3-319-18356-5_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18355-8

  • Online ISBN: 978-3-319-18356-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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