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Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models

2007

Conference Paper

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We consider a model to cluster the components of a vector time-series. The task is to assign each component of the vector time-series to a single cluster, basing this assignment on the simultaneous dynamical similarity of the component to other components in the cluster. This is in contrast to the more familiar task of clustering a set of time-series based on global measures of their similarity. The model is based on a Dirichlet Mixture of Linear Gaussian State-Space models (LGSSMs), in which each LGSSM is treated with a prior to encourage the simplest explanation. The resulting model is approximated using a ‘collapsed’ variational Bayes implementation.

Author(s): Chiappa, S. and Barber, D.
Book Title: ISPA 2007
Journal: Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis (ISPA 2007)
Pages: 446-451
Year: 2007
Month: September
Day: 0
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ISPA.2007.4383735
Event Name: 5th International Symposium on Image and Signal Processing and Analysis
Event Place: Istanbul, Turkey

Address: Los Alamitos, CA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4913,
  title = {Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models},
  author = {Chiappa, S. and Barber, D.},
  journal = {Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis (ISPA 2007)},
  booktitle = {ISPA 2007},
  pages = {446-451},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = sep,
  year = {2007},
  month_numeric = {9}
}