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The conventional methods are achieved by either the eigenvalue decomposition or the singular values decomposition (SVD).
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A PCA can be performed by either an eigenvalue decomposition of the covariance matrix Σ or by singular value decomposition (SVD) of the data matrix X.
As done in literature [3, 11, 25], the ASE is analyzed by deriving either the asymptotic eigenvalue distribution of the matrices in the form of HH H [11] or the asymptotic distribution of the SINR attained by the various detectors [3, 25].
We obtain a subspace by solving the eigenvalue problem: (4).
The two-degree-of-freedom flutter limit is computed by solving the Eigenvalue problem.
The parameters of these filters are obtained by solving the eigenvalue problem (EVP).
By solving the eigenvalue problem, natural frequencies of the structural system are obtained.
The fundamental frequencies are found by solving the eigenvalue problem.
Ratio of Eigenvalues is computed by dividing the eigenvalue for the first component by the eigenvalue for the second component for each subscale.
The adequate (smallest) number of factors was determined by using the Eigenvalue criteria.
It operates by calculating the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix.
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