Output Grouping using Dirichlet Mixtures of Linear Gaussian State-Space Models
2007
Conference Paper
ei
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): | Empirische Inferenz |
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}, doi = {10.1109/ISPA.2007.4383735}, month_numeric = {9} } |