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

Hosseini B, Hülsmann F, Botsch M, Hammer B (2016)
In: Artificial Neural Networks and Machine Learning – ICANN 2016. E.P. Villa A, Masulli P, Javier Pons Rivero A (Eds); Lecture Notes in Computer Science, 9887. Cham: Springer: 506-514.

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Conference Paper | Published | English
Editor
E.P. Villa, Alessandro ; Masulli, Paolo ; Javier Pons Rivero, Antonio
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.
Publishing Year
Conference
The 25th International Conference on Artificial Neural Networks (ICANN 2016)
Location
Barcelona
Conference Date
2016-09-06 – 2016-09-09
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Hosseini B, Hülsmann F, Botsch M, Hammer B. Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In: E.P. Villa A, Masulli P, Javier Pons Rivero A, eds. Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. Vol 9887. Cham: Springer; 2016: 506-514.
Hosseini, B., Hülsmann, F., Botsch, M., & Hammer, B. (2016). Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In A. E.P. Villa, P. Masulli, & A. Javier Pons Rivero (Eds.), Lecture Notes in Computer Science: Vol. 9887. Artificial Neural Networks and Machine Learning – ICANN 2016 (pp. 506-514). Cham: Springer. doi:10.1007/978-3-319-44781-0_60
Hosseini, B., Hülsmann, F., Botsch, M., and Hammer, B. (2016). “Non-Negative Kernel Sparse Coding for the Analysis of Motion Data” in Artificial Neural Networks and Machine Learning – ICANN 2016, E.P. Villa, A., Masulli, P., and Javier Pons Rivero, A. eds. Lecture Notes in Computer Science, vol. 9887, (Cham: Springer), 506-514.
Hosseini, B., et al., 2016. Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In A. E.P. Villa, P. Masulli, & A. Javier Pons Rivero, eds. Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. no.9887 Cham: Springer, pp. 506-514.
B. Hosseini, et al., “Non-Negative Kernel Sparse Coding for the Analysis of Motion Data”, Artificial Neural Networks and Machine Learning – ICANN 2016, A. E.P. Villa, P. Masulli, and A. Javier Pons Rivero, eds., Lecture Notes in Computer Science, vol. 9887, Cham: Springer, 2016, pp.506-514.
Hosseini, B., Hülsmann, F., Botsch, M., Hammer, B.: Non-Negative Kernel Sparse Coding for the Analysis of Motion Data. In: E.P. Villa, A., Masulli, P., and Javier Pons Rivero, A. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2016. Lecture Notes in Computer Science. 9887, p. 506-514. Springer, Cham (2016).
Hosseini, Babak, Hülsmann, Felix, Botsch, Mario, and Hammer, Barbara. “Non-Negative Kernel Sparse Coding for the Analysis of Motion Data”. Artificial Neural Networks and Machine Learning – ICANN 2016. Ed. Alessandro E.P. Villa, Paolo Masulli, and Antonio Javier Pons Rivero. Cham: Springer, 2016.Vol. 9887. Lecture Notes in Computer Science. 506-514.
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Matlab code: Non-negative Kernel Sparse coding
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The code for Non-Negative Kernel Sparse Coding algorithm (NNKSC )
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