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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 352358.0, MPI für biologische Kybernetik / Biologische Kybernetik
Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models
Authors:Chiappa, S.; Barber, D.
Date of Publication (YYYY-MM-DD):2007-09
Title of Proceedings:Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis (ISPA 2007)
Start Page:446
End Page:451
Physical Description:6
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:4913
URL:http://www.ieeexplore.ieee.org/iel5/4383644/438364...
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