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2006

Unsupervised clustering of continuous trajectories of kinematic trees with SOM-SD

9 years 11 months ago
Unsupervised clustering of continuous trajectories of kinematic trees with SOM-SD
We explore the capability of the Self Organizing Map for structured data (SOM-SD) to compress continuous time data recorded from a kinematic tree, which can represent a robot or an artifical stickfigure. We compare different encodings of this data as tree or sequence, which preserve the structural dependencies introduced by the physical constraints in the model to different degrees. Besides computing a standard quantization error, we propose a new measure to account for the amount of compression in the temporal domain based on correlation of the degree of locality of the tree and the number of winners in the map for this tree. The approach is demonstrated for a stick-figure moving in a physics based simulation world. It turns out that SOM-SD is able to achieve a very exact representation of the data together with a reasonable compression if tree encodings rather than sequence encodings are used.
Jochen J. Steil, Risto Koiva, Alessandro Sperduti
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2006
Where ESANN
Authors Jochen J. Steil, Risto Koiva, Alessandro Sperduti
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