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  Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models

Chiappa, S. (2007). Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models. Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis (ISPA 2007), 446-451.

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 Urheber:
Chiappa, S1, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Zusammenfassung: We consider a model to cluster the components of a vector time-series. The task is to assign each component of the vector time-series to a single cluster, basing this assignment on the simultaneous dynamical similarity of the component to other components in the cluster. This is in contrast to the more familiar task of clustering a set of time-series based on global measures of their similarity. The model is based on a Dirichlet Mixture of Linear Gaussian State-Space models (LGSSMs), in which each LGSSM is treated with a prior to encourage the simplest explanation. The resulting model is approximated using a ‘collapsed’ variational Bayes implementation.

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 Datum: 2007-09
 Publikationsstatus: Erschienen
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Titel: 5th International Symposium on Image and Signal Processing and Analysis
Veranstaltungsort: Istanbul, Turkey
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Titel: Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis (ISPA 2007)
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Los Alamitos, CA, USA : IEEE Computer Society
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 446 - 451 Identifikator: -