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・ Orthogonal collocation
・ Orthogonal complement
・ Orthogonal convex hull
・ Orthogonal coordinates
・ Orthogonal Defect Classification
・ Orthogonal diagonalization
・ Orthogonal frequency-division multiple access
・ Orthogonal frequency-division multiplexing
・ Orthogonal functions
・ Orthogonal group
・ Orthogonal instruction set
・ Orthogonal matrix
・ Orthogonal polarization spectral imaging
・ Orthogonal polynomials
・ Orthogonal polynomials on the unit circle
Orthogonal Procrustes problem
・ Orthogonal symmetric Lie algebra
・ Orthogonal trajectory
・ Orthogonal transformation
・ Orthogonal wavelet
・ Orthogonality
・ Orthogonality (programming)
・ Orthogonality (term rewriting)
・ Orthogonality principle
・ Orthogonalization
・ Orthogonia
・ Orthogonia grisea
・ Orthogonia plana
・ Orthogonia plumbinotata
・ Orthogoniinae


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Orthogonal Procrustes problem : ウィキペディア英語版
Orthogonal Procrustes problem
The orthogonal Procrustes problem is a matrix approximation problem in linear algebra. In its classical form, one is given two matrices A and B and asked to find an orthogonal matrix R which most closely maps A to B. Specifically,
:R = \arg\min_\Omega\|\Omega A-B\|_F \quad\mathrm\quad \Omega^T
\Omega=I,
where \|\cdot\|_F denotes the Frobenius norm.
The name Procrustes refers to a bandit from Greek mythology who made his victims fit his bed by either stretching their limbs or cutting them off.
== Solution ==
This problem was originally solved by Peter Schonemann in a 1964 thesis. The individual solution was later published. A proof is also given in
This problem is equivalent to finding the nearest orthogonal matrix to a given matrix M=A^B. To find this orthogonal matrix R, one uses the singular value decomposition
:M=U\Sigma V^T\,\!
to write
:R=VU^T.\,\!

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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