- Title
- Aligning manifolds of double pendulum dynamics under the influence of noise
- Creator
- Aziz, Fayeem; Wong, Aaron S. W.; Welsh, James S.; Chalup, Stephan K.
- Relation
- 25th International Conference on Neural Information Processing (ICONIP 2018). Neural Information Processing: 25th International Conference, ICONIP 2018 Siem Reap, Cambodia, December 13-16, 2018 Proceedings, Part VII [presented in Lecture Notes in Computer Science, Vol. 11307] (Siem Reap, Cambodia 13-16 December, 2018) p. 74-85
- Publisher Link
- http://dx.doi.org/10.1007/978-3-030-04239-4_7
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2018
- Description
- 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.
- Subject
- manifold learning; dimensionality reduction; manifold alignment; double pendulum; robot motion
- Identifier
- http://hdl.handle.net/1959.13/1403885
- Identifier
- uon:35229
- Identifier
- ISBN:9783030042387
- Language
- eng
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