PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Bayesian approach to switching linear Gaussian state-space models for unsupervised time-series segmentation
Silvia Chiappa
In: ICMLA 2008, 11-13 Dec 2008, San Diego, USA.

Abstract

Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Bayesian approach to a segmentation model based on the switching linear Gaussian state-space model that enforces a sparse parametrization, such as to use only a small number of a priori available different dynamics to explain the data. This enables us to estimate the number of segment-types within the model, in contrast to previous non-Bayesian approaches where training and comparing several separate models was required. As the resulting model is computationally intractable, we introduce a variational approximation where a reformulation of the problem enables the use of efficient inference algorithms.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5184
Deposited By:Silvia Chiappa
Deposited On:24 March 2009