How Can You Measure the Distance Between Two Subspaces in Higher Dimensions?

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SUMMARY

The discussion focuses on measuring the distance between two k-subspaces in higher dimensions, specifically within the context of \(\mathbb{R}^n\). The user proposes using the minimum angle between unit vectors as a distance metric, defined by the formula \(d(u,v)=\frac{||}{|u||v|}\). To extend this concept to higher dimensions, the user references the scalar product in Geometric (Clifford) Algebra, which utilizes determinants to compute angles between subspaces. The approach is validated as a correct method for defining distance between subspaces.

PREREQUISITES
  • Understanding of k-subspaces in \(\mathbb{R}^n\)
  • Familiarity with scalar products and their properties
  • Knowledge of Geometric (Clifford) Algebra
  • Basic linear algebra concepts, including determinants
NEXT STEPS
  • Research the properties of Grassmannians and their applications in geometry
  • Learn about the computation of angles between subspaces using determinants
  • Explore advanced topics in Geometric Algebra, focusing on scalar products
  • Investigate other distance metrics for subspaces in higher-dimensional spaces
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Mathematicians, physicists, and computer scientists interested in advanced geometry, linear algebra, and applications of Geometric Algebra in measuring distances between subspaces.

mnb96
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Hello.
Let´s suppose we are given two subspaces of [tex]\mathbb{R}^n[/tex] that have dimension k, where [itex]1\leq k<n[/itex]. I think they are called grassmanians.
How can I compute a "distance" between two different k-subspaces?


my attempt to a solution:
As a toy example, for n=2 and k=1 we can use the minimum angle between the unit-vectors u and v:

[tex]d(u,v)=\frac{|<u,v>|}{|u||v|}[/tex]

In order to extend this to more dimensions I used the definition of scalar product used in Geometric (Clifford) Algebra, which is:

[tex]\ast : \wedge\mathbb{R}^n \times \wedge\mathbb{R}^n \rightarrow \mathbb{R}[/tex]

[tex]A\ast B=(a_1\wedge\ldots\wedge a_k)\ast(b_1\wedge\ldots\wedge b_m)=det[m_{ij}][/tex] ; for [tex]k=m[/tex]
[tex]A\ast B=0[/tex] ; for [tex]k\neq m[/tex]

where the (i,j) element of that matrix is [tex]m_{ij}=<a_i,b_j>[/tex].
Is that correct?
 
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How do you define "distance"?
 
I thought of quantifying the "distance" between the subspaces with the minimum angle between them (see the toy-example in 2-dimensions in my first post).

The scalar product (and contraction) introduced in Geometric (Clifford) Algebra is used to compute the angle between subspaces of same (or different) dimensions.
 

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