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

Chiappa, S; Barber, D

doi:10.1109/ISPA.2007.4383735

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

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Chiappa, S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4383735
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Zitation

Chiappa, S., & Barber, D. (2007). Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models. In 2007 5th International Symposium on Image and Signal Processing and Analysis (pp. 446-451). Piscataway, NJ, USA: IEEE.

Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-CBF5-5

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.