Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix

DOI10.1016/j.jmva.2010.04.006zbMath1203.62112OpenAlexW2060651165MaRDI QIDQ990890

Makoto Aoshima, Kazuyoshi Yata

Publication date: 1 September 2010

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2010.04.006

zbMATH Keywords

consistencyprincipal component analysiseigenvalue distributionmixture modelsingular valueHDLSSmicroarray data analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Asymptotic distribution theory in statistics (62E20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Eigenvalues, singular values, and eigenvectors (15A18)

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Cites Work

• Unnamed Item
• PCA consistency in high dimension, low sample size context
• On the distribution of the largest eigenvalue in principal components analysis
• Eigenvalues of large sample covariance matrices of spiked population models
• Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices
• Intrinsic Dimensionality Estimation of High-Dimension, Low Sample Size Data withD-Asymptotics
• PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context
• On Consistency and Sparsity for Principal Components Analysis in High Dimensions
• Geometric Representation of High Dimension, Low Sample Size Data
• The high-dimension, low-sample-size geometric representation holds under mild conditions

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