Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models
Silvia Chiappa and David Barber
In: 5th International Symposium on Image and Signal Processing and Analysis, 27-29 September 2007, Istanbul, Turkey.

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.

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