Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets

@article{Kvinge2018MonitoringTS,
  title={Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large datasets},
  author={Henry Kvinge and Elin Farnell and Michael J. Kirby and Chris Peterson},
  journal={2018 IEEE International Conference on Big Data (Big Data)},
  year={2018},
  pages={1045-1051}
}
Dimensionality-reduction methods are a fundamental tool in the analysis of large datasets. These algorithms work on the assumption that the "intrinsic dimension" of the data is generally much smaller than the ambient dimension in which it is collected. Alongside their usual purpose of mapping data into a smaller-dimensional space with minimal information loss, dimensionality-reduction techniques implicitly or explicitly provide information about the dimension of the dataset.In this paper, we… 

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