TY  - EDBOOK
AB  - 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.
DA  - 2016
DO  - 10.1007/978-3-319-44781-0_60
KW  - Kernel sparse coding
KW  - Motion analysis
KW  - Classification
KW  - Interpretable models
KW  - Dynamic time warping
LA  - eng
PY  - 2016
SN  - 978-3-319-44780-3
TI  - Non-Negative Kernel Sparse Coding for the Analysis of Motion Data
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-29044695
Y2  - 2024-11-22T03:40:51
ER  -