Introduces a sampling pseudospectrum P(λ) and estimator ˆP(λ) obtained by reprocessing finite data to statistically test the location of true eigenvalues versus sampling artifacts in data-driven matrices.
An introduction to matrix concentration inequalities.Foundations and Trends in Machine Learning, 8(1-2):1–230, 2015
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Sampling pseudospectrum for data-driven matrices
Introduces a sampling pseudospectrum P(λ) and estimator ˆP(λ) obtained by reprocessing finite data to statistically test the location of true eigenvalues versus sampling artifacts in data-driven matrices.