Reconstruction of a high-dimensional low-rank matrix

DOI10.1214/16-EJS1128zbMath1341.62170MaRDI QIDQ276225

Kazuyoshi Yata, Makoto Aoshima

Publication date: 3 May 2016

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

Full work available at URL: https://projecteuclid.org/euclid.ejs/1460141647

zbMATH Keywords

singular value decompositionPCAeigenstructureHDLSSnoise-reduction methodology

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Factor analysis and principal components; correspondence analysis (62H25)

Related Items (2)

Unnamed Item ⋮ A survey of high dimension low sample size asymptotics

Cites Work

• Reconstruction of a low-rank matrix in the presence of Gaussian noise
• PCA consistency for the power spiked model in high-dimensional settings
• Estimation of high-dimensional low-rank matrices
• Estimation of (near) low-rank matrices with noise and high-dimensional scaling
• Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations
• Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context
• PCA consistency in high dimension, low sample size context
• Consistency of sparse PCA in high dimension, low sample size contexts
• High dimension low sample size asymptotics of robust PCA
• PCA Consistency for Non-Gaussian Data in High Dimension, Low Sample Size Context

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