Fusing separated representation into an autoencoder for magnetic materials outlier detection
In materials science, an outlier may be due to variability in measurement, or it may indicate experimental errors. In this paper, we used an unsupervised method to remove outliers before further data-driven material analysis. Recently, autoencoder networks have achieved excellent results by minimizi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor and Francis Ltd.
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |