Visualizing and Interpreting Unsupervised Solar Wind Classifications

Visualizing and Interpreting Unsupervised Solar Wind Classifications

One of the goals of machine learning is to eliminate tedious and arduous repetitive work. The manual and semi-automatic classification of millions of hours of solar wind data from multiple missions can be replaced by automatic algorithms that can discover, in mountains of multi-dimensional data, the...

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Bibliographic Details
Main Authors: Jorge Amaya, Romain Dupuis, Maria Elena Innocenti, Giovanni Lapenta
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-09-01
Series:Frontiers in Astronomy and Space Sciences
Subjects:
solar wind
ACE
Self-Organizing Maps
clustering
autoencoder
PCA
Online Access:https://www.frontiersin.org/article/10.3389/fspas.2020.553207/full

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