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We are interested in the decomposition of motion data into a sparse linearcombination of base functions which enable efficient data processing. Wecombine two prominent frameworks: dynamic time warping (DTW), which offersparticularly successful pairwise motion data comparison, and sparse coding(SC), which enables an automatic decomposition of vectorial data into a sparselinear combination of base vectors. We enhance SC as follows: an efficientkernelization which extends its application domain to general similarity datasuch as offered by DTW, and its restriction to non-negative linearrepresentations of signals and base vectors in order to guarantee a meaningfuldictionary. Empirical evaluations on motion capture benchmarks show theeffectiveness of our framework regarding interpretation and discriminationconcerns.

Non-Negative Kernel Sparse Coding for the Classification of Motion Data