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  A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation

Chiappa, S. (2008). A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation. Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA 2008), 3-9.

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Chiappa, S1, Autor           
Wani, Herausgeber
A., M., Herausgeber
Chen, X.-W., Herausgeber
Casasent, D., Herausgeber
Kurgan, L., Herausgeber
Hu, T., Herausgeber
Hafeez, K., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Bayesian approach to a segmentation model based on the switching linear Gaussian state-space model that enforces a sparse parametrization, such as to use only a small number of a priori available different dynamics to explain the data. This enables us to estimate the number of segment-types within the model, in contrast to previous non-Bayesian approaches where training and comparing several separate models was required. As the resulting model is computationally intractable, we introduce a variational approximation where a reformulation of the problem enables the use of efficient inference algorithms.

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 Datum: 2008-12
 Publikationsstatus: Erschienen
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 Identifikatoren: URI: http://www.icmla-conference.org/icmla08/
DOI: 10.1109/ICMLA.2008.109
BibTex Citekey: 5380
 Art des Abschluß: -

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Titel: 7th International Conference on Machine Learning and Applications
Veranstaltungsort: San Diego, CA, USA
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Titel: Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA 2008)
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Los Alamitos, CA, USA : IEEE Computer Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 3 - 9 Identifikator: -