Robust and sparse multigroup classification by the optimal scoring approach

DOI10.1007/s10618-019-00666-8zbMath1433.68372OpenAlexW3007146687MaRDI QIDQ127301

Peter Filzmoser, Irene Ortner, Christophe Croux, Christophe Croux, Irene Ortner, Peter Filzmoser

Publication date: 20 February 2020

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-019-00666-8

zbMATH Keywords

robustnesspenalizationvariable selectionhigh dimensional datalinear discriminant analysissupervised classification

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Robust and sparse multinomial regression in high dimensions ⋮ rrcovHD

Uses Software

• R
• sparseLDA
• penalizedLDA
• rrcovHD

Cites Work

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• High-breakdown robust multivariate methods
• Fast and robust discriminant analysis
• Least angle regression. (With discussion)
• Sparse least trimmed squares regression for analyzing high-dimensional large data sets
• Clustering of imbalanced high-dimensional media data
• Coordinate descent algorithms for lasso penalized regression
• Penalized Classification using Fisher’s Linear Discriminant
• Iteratively Reweighted Least Squares: Algorithms, Convergence Analysis, and Numerical Comparisons
• The Influence Curve and Its Role in Robust Estimation
• Flexible Discriminant Analysis by Optimal Scoring
• Regression Shrinkage and Selection via The Lasso: A Retrospective

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