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[ascl:2105.006] The Sequencer: Detect one-dimensional sequences in complex datasets

The Sequencer reveals the main sequence in a dataset if one exists. To do so, it reorders objects within a set to produce the most elongated manifold describing their similarities which are measured in a multi-scale manner and using a collection of metrics. To be generic, it combines information from four different metrics: the Euclidean Distance, the Kullback-Leibler Divergence, the Monge-Wasserstein or Earth Mover Distance, and the Energy Distance. It considers different scales of the data by dividing each object in the input data into separate parts (chunks), and estimating pair-wise similarities between the chunks. It then aggregates the information in each of the chunks into a single estimator for each metric+scale.

Code site:
http://sequencer.org/ https://github.com/dalya/Sequencer
Used in:
https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.3404B
Described in:
https://ui.adsabs.harvard.edu/abs/2020arXiv200613948B
Bibcode:
2021ascl.soft05006B

Views: 399

ascl:2105.006
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