Sample truncation strategies for outlier removal in geochemical data: the MCD robust distance approach versus t-SNE ensemble clustering

DOI10.1007/s11004-019-09839-zzbMath1458.86013OpenAlexW2990328657WikidataQ126652235 ScholiaQ126652235MaRDI QIDQ2022103

Raymond Leung, Mehala Balamurali, Arman Melkumyan

Publication date: 27 April 2021

Published in: Mathematical Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11004-019-09839-z

zbMATH Keywords

outlier detectionrobust distancegeochemical datasample truncation strategiessubdomain identification

Mathematics Subject Classification ID

Geostatistics (86A32) Computational methods for problems pertaining to geophysics (86-08)

Related Items (1)

Surface warping incorporating machine learning assisted domain likelihood estimation: a new paradigm in mine geology modeling and automation

Uses Software

• Silhouettes
• t-SNE
• gmGeostats

Cites Work

• Isometric logratio transformations for compositional data analysis
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• Geostatistics for compositional data: an overview
• Outlier detection for compositional data using robust methods
• Robust Estimation of Dispersion Matrices and Principal Components
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