@inproceedings{5380,
  title = {A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation},
  journal = {Proceedings of the 7th International Conference on Machine Learning and Applications (ICMLA 2008)},
  booktitle = {ICMLA 2008},
  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.},
  pages = {3-9},
  editors = {Wani, M. A., X.-W. Chen, D. Casasent, L. Kurgan, T. Hu, K. Hafeez},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = dec,
  year = {2008},
  author = {Chiappa, S.},
  doi = {10.1109/ICMLA.2008.109},
  month_numeric = {12}
}