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- W2952042838 abstract "How many samples are sufficient to guarantee that the eigenvectors and eigenvalues of the sample covariance matrix are close to those of the actual covariance matrix? For a wide family of distributions, including distributions with finite second moment and distributions supported in a centered Euclidean ball, we prove that the inner product between eigenvectors of the sample and actual covariance matrices decreases proportionally to the respective eigenvalue distance. Our findings imply non-asymptotic concentration bounds for eigenvectors, eigenspaces, and eigenvalues. They also provide conditions for distinguishing principal components based on a constant number of samples." @default.
- W2952042838 created "2019-06-27" @default.
- W2952042838 creator A5033465803 @default.
- W2952042838 date "2017-02-17" @default.
- W2952042838 modified "2023-10-01" @default.
- W2952042838 title "How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices" @default.
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