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

Babak Hosseini, Felix Hülsmann, Mario Botsch, Barbara Hammer

Abstract: 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.