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Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

  • Fayeem Aziz
  • Aaron S. W. Wong
  • James S. Welsh
  • Stephan K. Chalup
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)

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.

Keywords

Manifold learning Dimensionality reduction Manifold alignment Double pendulum Robot motion 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Fayeem Aziz
    • 1
  • Aaron S. W. Wong
    • 1
  • James S. Welsh
    • 1
  • Stephan K. Chalup
    • 1
  1. 1.School of Electrical Engineering and ComputingThe University of NewcastleCallaghanAustralia

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