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arXiv · 1903.03891

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

Babak Hosseini·Felix Hülsmann·Mario Botsch·Barbara Hammer
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Abstract

We are interested in the 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 as follows: an 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. Empirical evaluations on motion capture benchmarks show the effectiveness of our framework regarding interpretation and discrimination concerns.

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Babak Hosseini, Felix Hülsmann, Mario Botsch, Barbara Hammer. 2019-03-12. Non-Negative Kernel Sparse Coding for the Classification of Motion Data. https://arxiv.org/abs/1903.03891

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