Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections

@article{Takeishi2022DiscriminantDM,
  title={Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections},
  author={Naoya Takeishi and Keisuke Fujii and Koh Takeuchi and Yoshinobu Kawahara},
  journal={ArXiv},
  year={2022},
  volume={abs/2102.09973}
}
Extracting coherent patterns is one of the standard approaches towards understanding spatiotemporal data. Dynamic mode decomposition (DMD) is a powerful tool for extracting coherent patterns, but the original DMD and most of its variants do not consider label information, which is often available as side information of spatio-temporal data. In this work, we propose a new method for extracting distinctive coherent patterns from labeled spatio-temporal data collections, such that they contribute… 
1 Citations
Discovering spatiotemporal patterns of COVID-19 pandemic in South Korea
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This work develops a novel framework of compatible window-wise dynamic mode decomposition (CwDMD) for nonlinear infectious disease dynamics and reveals that the first wave was contained in the Daegu and Gyeongbuk areas, but it spread rapidly to the whole of South Korea after the second wave.

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