Gradient-Based Kernel Dimension Reduction for Regression

DOI10.1080/01621459.2013.838167zbMath1367.62118OpenAlexW2036163871MaRDI QIDQ4975356

Chenlei Leng, Kenji Fukumizu

Publication date: 4 August 2017

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2013.838167

zbMATH Keywords

kernel methodreproducing kernel Hilbert spaceconditional independencesufficient dimension reduction

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07)

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Cites Work

• Unnamed Item
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• Principal support vector machines for linear and nonlinear sufficient dimension reduction
• On regularization algorithms in learning theory
• Kernel methods in machine learning
• An RKHS formulation of the inverse regression dimension-reduction problem
• Optimal rates for the regularized least-squares algorithm
• Kernel dimension reduction in regression
• Asymptotic behavior of the eigenvalues of certain integral equations. II
• Shannon sampling. II: Connections to learning theory
• Contour regression: a general approach to dimension reduction
• Learning theory estimates via integral operators and their approximations
• A Note on Sliced Inverse Regression with Regularizations
• On Directional Regression for Dimension Reduction
• Extending Sliced Inverse Regression
• An Adaptive Estimation of Dimension Reduction Space
• Discussion of: Brownian distance covariance

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