Using adaptively weighted large margin classifiers for robust sufficient dimension reduction

DOI10.1080/02331888.2019.1636050zbMath1429.62233OpenAlexW2955654947WikidataQ127552710 ScholiaQ127552710MaRDI QIDQ5228862

Andreas Artemiou

Publication date: 13 August 2019

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: http://orca.cf.ac.uk/123584/1/Revision1_statistics.pdf

zbMATH Keywords

outliersdimension reductionsupport vector machinesadaptive weights

Mathematics Subject Classification ID

Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistics of extreme values; tail inference (62G32)

Uses Software

• UCI-ml

Cites Work

• Unnamed Item
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• A Cost Based Reweighted Scheme of Principal Support Vector Machine
• Probability-enhanced sufficient dimension reduction for binary classification
• On Directional Regression for Dimension Reduction
• Principal Hessian Directions Revisited
• Sliced Inverse Regression for Dimension Reduction
• On Principal Hessian Directions for Data Visualization and Dimension Reduction: Another Application of Stein's Lemma
• Principal weighted support vector machines for sufficient dimension reduction in binary classification
• Support vector machines for classification in nonstandard situations

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