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Let (hat {textbf {Z}}) be the projection matrix given by (4).
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(W in R^{m times c}) is the projection matrix.
The matrix Π Z in (20) is the projection matrix which can be approximated as (21).
where is the projection matrix associated to the th camera [10] and tilde denotes homogeneous coordinates.
where P Ω j i is the projection matrix of Ω j i.
where P s ˜ is the projection matrix onto s ˜, and P s ˜ ⊥ is the orthogonal complement of P s ˜.
where R = [R1R2... R d ] is the projection matrix and Y = [Y1Y2... Y d ] is the fractal feature matrix produced after applying 2DPCA.
If the interferences dominate the array covariance matrix, expressed as R ̂ xx − 1 ≃ σ − 2 P H i ⊥, where P H i ⊥ is the projection matrix to the subspace orthogonal to the interference subspace, then the latter expression in (18) arises.
More specifically, given a matrix Z ∈ ℂN × M, we need to find out two matrices W ∈ ℂN × K and H ∈ ℂK × M to minimize ( {leftVert mathbf{Z}-mathbf{WH}rightVert}_F^2 mathrm{s}.mathrm{t} mathbf{H}=mathbf{VZ} ) where V ∈ ℂK × N is the projection matrix.
The theory is as follows: P′ is the projection matrix from the plane calibration board's coordinate system to the omnidirectional camera coordinate system, and V 1 ' = [q 0, q 1, q 2, q 3, t 1, t 2, t 3] contains the unknown variables.
Under this loss function, the RR estimator of β is given by (3) β ^ RR = X ⊤ M Z X + λ I − 1 X ⊤ M Z y, where M Z = I − Z(Z ⊤ Z −1 Z ⊤ is the projection matrix, removing the effects of the confounding variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com