Published on Wed Sep 19 2018

Aligning Manifolds of Double Pendulum Dynamics Under the Influence of Noise

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

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.

0
0
0
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.