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Proc. 17th European Symposium on Artificial Neural Networks (ESANN ’09)

dc.contributor.authorBitzer, Sebastian
dc.contributor.authorKlanke, Stefan
dc.contributor.authorVijayakumar, Sethu
dc.date.accessioned2010-08-24T10:01:29Z
dc.date.available2010-08-24T10:01:29Z
dc.date.issued2009
dc.identifier.urihttp://www.dice.ucl.ac.be/esann/proceedings/papers.php?ann=2009en
dc.identifier.urihttp://hdl.handle.net/1842/3675
dc.description.abstractIn 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 for which we know the ground truth to evaluate a range of nonlinear dimensionality reduction methods with respect to the quality of motion interpolation. We show that results are extremely sensitive to parameter settings and data set used, but that dimensionality reduction can potentially improve the quality of linear motion interpolation, in particular in the presence of noise.en
dc.language.isoenen
dc.subjectInformaticsen
dc.subjectComputer Scienceen
dc.titleDoes Dimensionality Reduction improve the Quality of Motion Interpolation?en
dc.typeConference Paperen
rps.titleProc. 17th European Symposium on Artificial Neural Networks (ESANN ’09)en
dc.extent.noOfPages6en
dc.date.updated2010-08-24T10:01:30Z


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