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Conference Paper

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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83858

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

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-CBF5-5
Abstract
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.