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Manifold-Based Classifier Ensembles

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Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

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Abstract

In this paper, we briefly present classifier ensembles making use of nonlinear manifolds. Riemannian manifolds have been created using classifier interactions which are presented as symmetric and positive-definite (SPD) matrices. Grassmann manifolds as some particular case of Riemannian manifolds are constructed using decision profiles. Experimental routine shows advantages of Riemannian geometry and nonlinear manifolds for classifier ensemble learning.

This research was supported by the Natural Sciences and Engineering Research Council of Canada.

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Acknowledgements

The Authors would like to thank the anonymous referees for valuable comments which helped to improve the paper.

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada

    Vitaliy Tayanov, Adam Krzyzak & Ching Y. Suen

Authors
  1. Vitaliy Tayanov
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  2. Adam Krzyzak
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  3. Ching Y. Suen
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Corresponding author

Correspondence to Adam Krzyzak .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

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Tayanov, V., Krzyzak, A., Suen, C.Y. (2020). Manifold-Based Classifier Ensembles. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_25

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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