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2008

A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation

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A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation
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 nonBayesian 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.
Silvia Chiappa
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where ICMLA
Authors Silvia Chiappa
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