The Sequencer: Detect one-dimensional sequences in complex datasets

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Ada Coda
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The Sequencer: Detect one-dimensional sequences in complex datasets

Post by Ada Coda » Sun May 30, 2021 5:34 pm

The Sequencer: Detect one-dimensional sequences in complex datasets

Abstract: 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.

Credit: Baron, Dalya; Ménard, Brice

Site: https://github.com/dalya/Sequencer
https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.3404B

Bibcode: 2021ascl.soft05006B

Preferred citation method: https://ui.adsabs.harvard.edu/abs/2020arXiv200613948B

ID: ascl:2105.006

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