Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series

Hosseini B, Hammer B (2019)
In: Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Verleysen M (Ed); .

Konferenzbeitrag | Englisch
 
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Herausgeber*in
Verleysen, Michel
Abstract / Bemerkung
There exist many approaches for description and recognition of unseen classes in datasets. Nevertheless, it becomes a challenging problem when we deal with multivariate time-series (MTS) (e.g., motion data), where we cannot apply the vectorial algorithms directly to the inputs. In this work, we propose a novel multiple-kernel dictionary learning (MKD) which learns semantic attributes based on specific combinations of MTS dimensions in the feature space. Hence, MKD can fully/partially reconstructs the unseen classes based on the training data (seen classes). Furthermore, we obtain sparse encodings for unseen classes based on the learned MKD attributes, and upon which we propose a simple but effective incremental clustering algorithm to categorize the unseen MTS classes in an unsupervised way. According to the empirical evaluation of our MKD framework on real benchmarks, it provides an interpretable reconstruction of unseen MTS data as well as a high performance regarding their online clustering.
Stichworte
Multivariate time-series; dictionary learning; unseen classes
Erscheinungsjahr
2019
Titel des Konferenzbandes
Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019)
Konferenz
European Symposium on Artificial Neural Networks (ESANN 2019)
Konferenzort
Bruges
Konferenzdatum
2019-04-24 – 2019-04-26
Page URI
https://pub.uni-bielefeld.de/record/2930303

Zitieren

Hosseini B, Hammer B. Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In: Verleysen M, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). 2019.
Hosseini, B., & Hammer, B. (2019). Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In M. Verleysen (Ed.), Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019)
Hosseini, B., and Hammer, B. (2019). “Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series” in Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), Verleysen, M. ed.
Hosseini, B., & Hammer, B., 2019. Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In M. Verleysen, ed. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019).
B. Hosseini and B. Hammer, “Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series”, Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019), M. Verleysen, ed., 2019.
Hosseini, B., Hammer, B.: Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series. In: Verleysen, M. (ed.) Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). (2019).
Hosseini, Babak, and Hammer, Barbara. “Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series”. Proceedings of the 27th European Symposium on Artificial Neural Networks (ESANN 2019). Ed. Michel Verleysen. 2019.

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arXiv: 1903.01867

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