| 11. | Performing PCA directly on the covariance matrix of the images is often computationally infeasible.
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| 12. | Exploiting the Hermitian symmetry of the covariance matrix R _ v, we can write
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| 13. | Consequently, the covariance matrix of the reduced VAR
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| 14. | Pairwise dependencies between the variables in the distribution are represented by a covariance matrix.
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| 15. | This ensures that the covariance matrix will accurately represent the distribution of the errors.
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| 16. | LMC, process convolution ) used to compute the multi-output covariance matrix.
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| 17. | Consequently, the virtue of a robust covariance matrix in this setting is unclear .
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| 18. | A simple version of a shrinkage estimator of the covariance matrix is constructed as follows.
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| 19. | However, maintaining the covariance matrix is not feasible computationally for high-dimensional systems.
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| 20. | Conversely, every positive semi-definite matrix is the covariance matrix of some multivariate distribution.
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