PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach
Silvia Chiappa and David Barber
(2007) Technical Report. Max-Planck Istitute for Biological Cybernetics, Tuebingen, Germany.

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.

EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:3547
Deposited By:Silvia Chiappa
Deposited On:11 February 2008