The LICORS cabinet: Nonparametric light cone methods for spatio-temporal modeling

@article{Montanez2017TheLC,
  title={The LICORS cabinet: Nonparametric light cone methods for spatio-temporal modeling},
  author={George D. Montanez and C. Shalizi},
  journal={2017 International Joint Conference on Neural Networks (IJCNN)},
  year={2017},
  pages={2811-2819}
}
  • George D. Montanez, C. Shalizi
  • Published 2017
  • Mathematics, Computer Science
  • 2017 International Joint Conference on Neural Networks (IJCNN)
  • Spatio-temporal data is intrinsically high dimensional, so unsupervised modeling is only feasible if we can exploit structure in the process. When the dynamics are local in both space and time, this structure can be exploited by splitting the global field into many lower-dimensional “light cones”. We review light cone decompositions for predictive state reconstruction, introducing three simple light cone algorithms. These methods allow for tractable inference of spatio-temporal data, such as… CONTINUE READING

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