Non-negative Kernel Sparse Coding for the Analysis of Motion Data

  • Babak Hosseini
  • Felix Hülsmann
  • Mario Botsch
  • Barbara Hammer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9887)


We are interested in a decomposition of motion data into a sparse linear combination of base functions which enable efficient data processing. We combine two prominent frameworks: dynamic time warping (DTW), which offers particularly successful pairwise motion data comparison, and sparse coding (SC), which enables an automatic decomposition of vectorial data into a sparse linear combination of base vectors. We enhance SC via efficient kernelization which extends its application domain to general similarity data such as offered by DTW, and its restriction to non-negative linear representations of signals and base vectors in order to guarantee a meaningful dictionary. We also implemented the proposed method in a classification framework and evaluated its performance on various motion capture benchmark data sets.


Kernel sparse coding Motion analysis Classification Interpretable models Dynamic time warping 



This research was supported by the Cluster of Excellence Cognitive Interaction Technology ‘CITEC’ (EXC 277) at Bielefeld University, which is funded by the German Research Foundation (DFG).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Babak Hosseini
    • 1
  • Felix Hülsmann
    • 1
  • Mario Botsch
    • 1
  • Barbara Hammer
    • 1
  1. 1.CITEC Centre of ExcellenceBielefeld UniversityBielefeldGermany

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