Non-Negative Kernel Sparse Coding for the Classification of Motion Data
This work addresses motion data classification, but appears incremental as it combines existing frameworks (DTW and SC) with modifications.
The authors tackled the problem of decomposing motion data into sparse linear combinations of base functions by combining dynamic time warping (DTW) and sparse coding (SC), enhancing SC with kernelization and non-negative representations. Empirical evaluations on motion capture benchmarks demonstrated effectiveness in interpretation and discrimination.
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.