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Conference Paper

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

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons83858

Chiappa,  S
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Chiappa, S. (2008). A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation. Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA 2008), 3-9.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C627-0
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.