Principle component analysis: robust versions

DOI10.1134/S0005117917030092zbMath1392.62176OpenAlexW2591785087MaRDI QIDQ2399473

M. V. Khlebnikov, Boris T. Polyak

Publication date: 23 August 2017

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0005117917030092

zbMATH Keywords

outliersrobustnessprincipal component analysisiteratively reweighted least squarescontaminated Gaussian distribution

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Nonparametric robustness (62G35)

Related Items (7)

Randomization and entropy in machine learning and data processing ⋮ Entropy-randomized projection ⋮ Entropy dimension reduction method for randomized machine learning problems ⋮ Кросс-энтропийная редукции матрицы данных с ограничением информационной емкости матриц-проекторов и их норм ⋮ Robust modifications of U-statistics and applications to covariance estimation problems ⋮ Comparative analysis of robust and classical methods for estimating the parameters of a threshold autoregression equation ⋮ Cross-entropy optimal dimensionality reduction with a condition on information capacity

Uses Software

• UCI-ml
• DASL

Cites Work

• Asymmetric distances, semidirected networks and majority in Fermat-Weber problems
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• From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
• Principal component analysis based on robust estimators of the covariance or correlation matrix: influence functions and efficiencies
• Robust Statistics
• NON-NORMALITY AND TESTS ON VARIANCES
• Robust Statistics
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