Sample Truncation Strategies for Outlier Removal in Geochemical Data: The MCD Robust Distance Approach Versus t-SNE Ensemble Clustering

@article{Leung2019SampleTS,
  title={Sample Truncation Strategies for Outlier Removal in Geochemical Data: The MCD Robust Distance Approach Versus t-SNE Ensemble Clustering},
  author={Raymond Leung and Mehala Balamurali and Arman Melkumyan},
  journal={Mathematical Geosciences},
  year={2019},
  volume={53},
  pages={105-130}
}
Abstract The presence of outliers in geochemical data can impact the accuracy of grade models and influence the interpretation of mine assay data. Removal of outliers is therefore an important consideration in grade estimation work. This paper presents two sample truncation strategies which have been devised to reject outliers in multivariate geochemical data. In essence, a data-dependent threshold is applied to the robust distances of sorted samples to discard outliers within a given class… 

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