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

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.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C627-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0003-31AC-4
Genre: Conference Paper

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4724948 (Publisher version)
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 Creators:
Chiappa, S1, 2, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2008-12
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/ICMLA.2008.109
BibTex Citekey: 5380
 Degree: -

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Title: 7th International Conference on Machine Learning and Applications
Place of Event: San Diego, CA, USA
Start-/End Date: 2008-12-11 - 2008-12-13

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Title: 2008 Seventh International Conference on Machine Learning and Applications
Source Genre: Proceedings
 Creator(s):
Wani, AM, Editor
Chen, X-W, Editor
Casasent, D, Editor
Curgan, L, Editor
Hu, T, Editor
Hafeez, K, Editor
Affiliations:
-
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3 - 9 Identifier: ISBN: 978-0-7695-3495-4