2020
DOI: 10.1007/s10208-020-09459-8
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The Grassmannian of affine subspaces

Abstract: The degree of the Grassmannian with respect to the Plücker embedding is well-known. However, the Plücker embedding, while ubiquitous in pure mathematics, is almost never used in applied mathematics. In applied mathematics, the Grassmannian is usually embedded as projection matrices Gr(k, R n ) ∼ = {P ∈ R n×n : P T = P = P 2 , tr(P ) = k} or as involution matrices Gr(k, R n ) ∼ = {X ∈ R n×n : X T = X, X 2 = I, tr(X) = 2k − n}. We will determine an explicit expression for the degree of the Grassmannian with resp… Show more

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Cited by 13 publications
(9 citation statements)
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“…Therefore, we think that Euclidean distance minimization in an affine patch is less meaningful than minimization relative to other distance measures. One option is to use a distance in the affine Grassmannian [LWY21], which is the space of lines in C 2 . This would take into account the above arguments that image points are usually given in affine coordinates.…”
Section: Multidegreesmentioning
confidence: 99%
“…Therefore, we think that Euclidean distance minimization in an affine patch is less meaningful than minimization relative to other distance measures. One option is to use a distance in the affine Grassmannian [LWY21], which is the space of lines in C 2 . This would take into account the above arguments that image points are usually given in affine coordinates.…”
Section: Multidegreesmentioning
confidence: 99%
“…We are specifically interested in affine subspaces of R 3 , e.g., lines and planes that are at some distance away from the origin. In analogy to Gr(k, n), the set of k-dimensional affine subspaces constitute a smooth manifold called the affine Grassmannian, denoted Graff(k, n) [44]. We write an element of this manifold as…”
Section: A Preliminariesmentioning
confidence: 99%
“…The Grassmannian G(N,M ) can be seen as the manifold built of the symmetric projection matrices of size M ×M and rank N [8]. In other words, we can represent the column space of a full rank channel H k as a point Pk ∈ G(N k ,M ), where Pk is the projection matrix…”
Section: B Principal Angles and Grassmann Manifoldmentioning
confidence: 99%