PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3549
Deposited By:Silvia Chiappa
Deposited On:11 February 2008