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.

Konferenzbeitrag | Veröffentlicht | Englisch
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Abstract / Bemerkung
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.
Erscheinungsjahr
Titel des Konferenzbandes
Artificial Neural Networks and Machine Learning – ICANN 2016
Band
9887
Seite(n)
506-514
Konferenz
The 25th International Conference on Artificial Neural Networks (ICANN 2016)
Konferenzort
Barcelona
Konferenzdatum
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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