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Applying matrix perturbation theory through a first order Taylor series expansion about the mean values of the uncertain parameters' conservative dynamic response bounds are obtained assuming a small degree of parameter uncertainty.
Note that (43) follows from applying matrix inversion lemmaa.
We strengthen our findings by separately applying matrix decomposition, supervised learning, and unsupervised learning.
Then the mathematical expression for parameter sensitivity matrix is obtained by applying matrix sensitivity analysis (PSM).
Applying matrix theory, it is obvious that (lambda_{j}>0) for all (jinmathbf{Z}(1,k)).
We showed that by applying matrix factorization techniques, i.e., SVD and NMF, this part of the AGC noise can be removed; moreover, it turns out that NMF outperforms SVD.
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Before applying matrices, a fluorescence image of the analyte was acquired and followed by recording the location of the specimen on the XYZ stage.
Previous work has applied matrix sketching to speed up the least squares regression (LSR) on the n >> d data.
In harmonic vibration, the applied matrix method is analogous to the stiffness method in structural mechanics.
Note that instead, references [1] and [2] in the introduction apply matrix permutations on the left-hand side of the matrix of code sequences.
In order to do this we have applied matrix factorization techniques, i.e., SVD (singular value decomposition) and NMF (non-negative matrix factorization), which enables signal space analysis.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com