Evaluating the performance of sparse principal component analysis methods in high-dimensional data scenarios

DOI10.1080/03610918.2015.1004268zbMath1368.62135OpenAlexW2562584879MaRDI QIDQ4976570

Joseph Beyene, Ashley J. Bonner

Publication date: 31 July 2017

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2015.1004268

zbMATH Keywords

loadingssimulationgene expressiontuning parametersparse PCA

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Measures of association (correlation, canonical correlation, etc.) (62H20)

Cites Work

• Unnamed Item
• Sparse principal component analysis via regularized low rank matrix approximation
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Regularization and Variable Selection Via the Elastic Net

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