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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 419999.0, MPI für biologische Kybernetik / Biologische Kybernetik
A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation
Authors:Chiappa, S.
Editors:Wani, M. A.; Chen, X.-W.; Casasent, D.; Kurgan, L.; Hu, T.; Hafeez, K.
Date of Publication (YYYY-MM-DD):2008-12
Title of Proceedings:Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA 2008)
Start Page:3
End Page:9
Physical Description:7
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
Identifiers:LOCALID:5380
URL:http://www.icmla-conference.org/icmla08/
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