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A Fast MBO Scheme for Multiclass Data Classification

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Scale Space and Variational Methods in Computer Vision (SSVM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

Abstract

We describe a new variant of the MBO scheme for solving the semi-supervised data classification problem on a weighted graph. The scheme is based on the minimization of the graph heat content energy. The resulting algorithms guarantee dissipation of the graph heat content energy for an extremely wide class of weight matrices. As a result, our method is both flexible and unconditionally stable. Experimental results on benchmark machine learning datasets show that our approach matches or exceeds the performance of current state-of-the-art variational methods while being considerably faster.

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Acknowledgments

The author is grateful to Selim Esedoḡlu for helpful comments and suggestions. The author was supported by NSF DMS-1317730.

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Authors and Affiliations

  1. University of Michigan, Ann Arbor, USA

    Matt Jacobs

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  1. Matt Jacobs
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Correspondence to Matt Jacobs .

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Editors and Affiliations

  1. University of Copenhagen , Copenhagen, Denmark

    François Lauze

  2. Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong

  3. Technical University of Denmark , Kongens Lyngby, Denmark

    Anders Bjorholm Dahl

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Jacobs, M. (2017). A Fast MBO Scheme for Multiclass Data Classification. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_27

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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