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• W3102111841 startingPage "50001" @default.
• W3102111841 abstract "We consider the problem of estimating the principal components of a population correlation matrix from a limited number of measurement data. Using a combination of random matrix and information-theoretic tools, we show that all the eigenmodes of the sample correlation matrices are informative, and not only the top ones. We show how this information can be exploited when prior information about the principal component, such as whether it is localized or not, is available by mapping the estimation problem onto the search for the ground state of a spin-glass-like effective Hamiltonian encoding the prior. Results are illustrated numerically on the spiked covariance model." @default.
• W3102111841 created "2020-11-23" @default.
• W3102111841 creator A5016002177 @default.
• W3102111841 creator A5081721028 @default.
• W3102111841 date "2015-12-01" @default.
• W3102111841 modified "2023-10-18" @default.
• W3102111841 title "Estimating the principal components of correlation matrices from all their empirical eigenvectors" @default.
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• W3102111841 doi "https://doi.org/10.1209/0295-5075/112/50001" @default.
• W3102111841 hasPublicationYear "2015" @default.
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