Inferring End‐Members From Geoscience Data Using Simplex Projected Gradient Descent‐Archetypal Analysis
Abstract End‐member mixing analysis (EMMA) is widely used to analyze geoscience data for their end‐members and mixing proportions. Many traditional EMMA methods depend on known end‐members, which are sometimes uncertain or unknown. Unsupervised EMMA methods infer end‐members from data, but many exis...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024JH000540 |
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