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

Chiappa, S

doi:10.1109/ICMLA.2008.109

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

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

MPS-Authors

/persons/resource/persons83858

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

External Resource

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4724948
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Chiappa, S. (2008). A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation. In A. Wani, X.-W. Chen, D. Casasent, L. Curgan, T. Hu, & K. Hafeez (Eds.), 2008 Seventh International Conference on Machine Learning and Applications (pp. 3-9). Piscataway, NJ, USA: IEEE.

Cite as: https://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.