Sparse Principal Component Analysis in Hilbert Space

DOI10.1111/sjos.12106zbMath1364.62139OpenAlexW1539830254MaRDI QIDQ5177962

Xin Qi, Ruiyan Luo

Publication date: 9 March 2015

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/sjos.12106

zbMATH Keywords

Hilbert spacehigh-dimensional datadimension reduction and feature selectionsparse PCA in Hilbert space

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25)

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Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety

Cites Work

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• High-dimensional analysis of semidefinite relaxations for sparse principal components
• Sparse principal component analysis via regularized low rank matrix approximation
• Projected gradient approach to the numerical solution of the SCoTLASS
• Finite sample approximation results for principal component analysis: A matrix perturbation approach
• PCA consistency in high dimension, low sample size context
• Principal component analysis.
• Sparse principal component analysis by choice of norm
• Functional data analysis.
• Generalized power method for sparse principal component analysis
• A Proof of Convergence of the Concave-Convex Procedure Using Zangwill's Theory
• Functional Data Analysis with R and MATLAB
• The Concave-Convex Procedure
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• A Direct Formulation for Sparse PCA Using Semidefinite Programming

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