Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach
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We describe two related models to cluster multidimensional time-series under the assumption of an underlying linear Gaussian dynamical process. In the first model, times-series are assigned to the same cluster when they show global similarity in their dynamics, while in the second model times-series are assigned to the same cluster when they show simultaneous similarity. Both models are based on Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models in order to (semi) automatically determine an appropriate number of components in the mixture, and to additionally bias the components to a parsimonious parameterization. The resulting models are formally intractable and to deal with this we describe a deterministic approximation based on a novel implementation of Variational Bayes.
| Author(s): | Chiappa, S. and Barber, D. |
| Links: | |
| Number (issue): | 161 |
| Year: | 2007 |
| Month: | March |
| Day: | 0 |
| BibTeX Type: | Technical Report (techreport) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Institution: | Max Planck Institute for Biological Cybernetics, Tübingen, Germany |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@techreport{4917,
title = {Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach},
abstract = {We describe two related models to cluster multidimensional time-series under the assumption of an underlying linear Gaussian dynamical process. In the first model, times-series are assigned to the same cluster when they show global similarity in their dynamics, while in the second model times-series are assigned to the same cluster when they show simultaneous similarity. Both models are based on Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models in order to (semi) automatically determine an appropriate number of components in the mixture, and to additionally bias the components to a parsimonious parameterization. The resulting models are formally intractable and to deal with this we describe a deterministic approximation based on a novel implementation of Variational Bayes.},
number = {161},
organization = {Max-Planck-Gesellschaft},
institution = {Max Planck Institute for Biological Cybernetics, Tübingen, Germany},
school = {Biologische Kybernetik},
month = mar,
year = {2007},
author = {Chiappa, S. and Barber, D.},
month_numeric = {3}
}