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van der Veen, A.J. (author), Romme, J.P.A. (author), Cui, Ye (author)
In Principal Component Analysis (PCA), the dimension of the signal subspace is detected by counting the number of eigenvalues of a covariance matrix that are above a threshold. Random matrix theory provides accurate estimates for this threshold if the underlying data matrix has independent identically distributed columns. However, in time series...
conference paper 2020