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