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 stick- figure. We compare different encodings of this data as tree or sequence, which preserve the structural dependencies introduced by the physical con- straints 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.