Domain-Agnostic Outlier Ranking Algorithms—A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets

Automatic detection of outliers is universally needed when working with scientific datasets, e.g., for cleaning datasets or flagging novel samples to guide instrument acquisition or scientific analysis. We present Domain-agnostic Outlier Ranking Algorithms (DORA), a configurable pipeline that facili...

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Bibliographic Details

Published in:	Frontiers in Astronomy and Space Sciences
Main Authors:	Hannah R. Kerner, Umaa Rebbapragada, Kiri L. Wagstaff, Steven Lu, Bryce Dubayah, Eric Huff, Jake Lee, Vinay Raman, Sakshum Kulshrestha
Format:	Article
Language:	English
Published:	Frontiers Media S.A. 2022-05-01
Subjects:	astrophysics planetary science Earth Science outlier detection novelty detection out-of-distribution detection
Online Access:	https://www.frontiersin.org/articles/10.3389/fspas.2022.867947/full