  We investigate metric learning in the context of dynamic time warping (DTW),
the by far most popular dissimilarity measure used for the comparison and
analysis of motion capture data. While metric learning enables a
problem-adapted representation of data, the majority of methods has been
proposed for vectorial data only. In this contribution, we extend the popular
principle offered by the large margin nearest neighbors learner (LMNN) to DTW
by treating the resulting component-wise dissimilarity values as features. We
demonstrate that this principle greatly enhances the classification accuracy in
several benchmarks. Further, we show that recent auxiliary concepts such as
metric regularization can be transferred from the vectorial case to
component-wise DTW in a similar way. We illustrate that metric regularization
constitutes a crucial prerequisite for the interpretation of the resulting
relevance profiles.
