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Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11307))

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Abstract

This study presents the results of a series of simulation experiments that evaluate and compare four different manifold alignment methods under the influence of noise. The data was created by simulating the dynamics of two slightly different double pendulums in three-dimensional space. The method of semi-supervised feature-level manifold alignment using global distance resulted in the most convincing visualisations. However, the semi-supervised feature-level local alignment methods resulted in smaller alignment errors. These local alignment methods were also more robust to noise and faster than the other methods.

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Acknowledgements

FA was supported by a UNRSC50:50 PhD scholarship at the University of Newcastle, Australia. The authors are grateful to the UON ARCS team who facilitated access to the UON high performance computing system.

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Authors and Affiliations

  1. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Fayeem Aziz, Aaron S. W. Wong, James S. Welsh & Stephan K. Chalup

Authors
  1. Fayeem Aziz
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  2. Aaron S. W. Wong
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  3. James S. Welsh
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  4. Stephan K. Chalup
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Corresponding author

Correspondence to Fayeem Aziz .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Aziz, F., Wong, A.S.W., Welsh, J.S., Chalup, S.K. (2018). Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-04239-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04238-7

  • Online ISBN: 978-3-030-04239-4

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