PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Does dimensionality reduction improve the quality of motion interpolation?
Sebastian Bitzer, Stefan Klanke and Sethu Vijayakumar
In: ESANN 2009, Bruges, Belgium(2009).

Abstract

In recent years nonlinear dimensionality reduction has frequently been suggested for the modelling of high-dimensional motion data. While it is intuitively plausible to use dimensionality reduction to recover low dimensional manifolds which compactly represent a given set of movements, there is a lack of critical investigation into the quality of resulting representations, in particular with respect to generalisability. Furthermore it is unclear how consistently particular methods can achieve good results. Here we use a set of robotic motion data of which we know ground truth to evaluate a range of nonlinear dimensionality reduction methods with respect to the quality of motion interpolation. We show that results are sensitive to parameter settings and data set used and that no dimensionality reduction method significantly outperforms naive interpolation.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4648
Deposited By:Stefan Klanke
Deposited On:08 March 2010