Principal minimax support vector machine for sufficient dimension reduction with contaminated data

DOI10.1016/j.csda.2015.06.011zbMath1468.62240OpenAlexW1267224911MaRDI QIDQ1660132

JingKe Zhou, Li Xing Zhu

Publication date: 15 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2015.06.011

zbMATH Keywords

sparse sufficient dimension reductionminimax robust support vector machinesrobust sufficient dimension reductiontransformed sufficient dimension reduction

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Factor analysis and principal components; correspondence analysis (62H25) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Dimension reduction based on conditional multiple index density function

Uses Software

• ARC

Cites Work

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• Efficient estimation in sufficient dimension reduction
• Principal support vector machines for linear and nonlinear sufficient dimension reduction
• Improved variable selection with forward-lasso adaptive shrinkage
• Robust dimension reduction based on canonical correlation
• Robust estimating equation-based sufficient dimension reduction
• Sparse sufficient dimension reduction using optimal scoring
• Contour regression: a general approach to dimension reduction
• A new family of power transformations to improve normality or symmetry
• Generalized power method for sparse principal component analysis
• Transformed sufficient dimension reduction
• Support Vector Machines with the Ramp Loss and the Hard Margin Loss
• A note On outlier sensitivity of Sliced Inverse Regression
• Sufficient dimension reduction through discretization-expectation estimation
• Sliced Inverse Regression for Dimension Reduction
• High Dimensional Variable Selection via Tilting
• An Adaptive Estimation of Dimension Reduction Space
• A Semiparametric Approach to Dimension Reduction
• Regularization and Variable Selection Via the Elastic Net
• Sparse sufficient dimension reduction
• Comment