Pith. sign in

REVIEW 1 cited by

Grassmann angles between real or complex subspaces

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1910.00147 v5 pith:PNF37RIX submitted 2019-09-30 math.MG

classification math.MG
keywords anglecomplexanglesgrassmannrealsubspacesalgebraasymmetric
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

The Grassmann angle improves upon similar angles between subspaces that measure volume contraction in orthogonal projections. It works in real or complex spaces, with important differences, and is asymmetric, what makes it more efficient when dimensions are distinct. It can be seen as an angle in Grassmann algebra, being related to its products and those of Clifford algebra, and gives the Fubini-Study metric on Grassmannians, an asymmetric metric on the full Grassmannian, and Hausdorff distances between full sub-Grassmannians. We give formulas for computing it in arbitrary bases, and identities for angles with certain families of subspaces, some of which are linked to real and complex Pythagorean theorems for volumes and quantum probabilities. Unusual features of the angle with an orthogonal complement, or the angle in complex spaces, are examined.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Geodesic Flow Kernels for Semi-Supervised Learning on Mixed-Variable Tabular Dataset

    cs.LG 2024-12 conditional novelty 5.0 of 10

    GFTab, a semi-supervised tabular method with variable-specific corruptions and geodesic flow kernel similarity, reports the best F1 on about half of 21 mixed-variable benchmarks with sparse labels.

Pith tools