Abstract: The talk is based on the papers [1,2,3] extending
Topological Data Analysis (TDA) to a wider area of Geometric Data
Science, which aims to continuously parametrise moduli spaces of real
objects under practical equivalences. The key example is a cloud A of
unordered points under isometry in R^n. Standard filtrations
(Vietoris-Rips, Cech, Delaunay) of complexes on A are invariant under
isometry (any distance-preserving transformation). Hence, persistent
homology of these filtrations can be considered a partial solution to
the following geo-mapping problem: design an invariant I of clouds of
m unordered points satisfying the conditions below. (a) Completeness:
any clouds A,B in R^n are related by rigid motion if and only if
I(A)=I(B); (b) Realisability: the invariant space {I(A) for all clouds
A in R^n} is explicitly parameterised so that any sampled value I(A)
can be realised by a cloud A, uniquely under motion in R^n; (c)
Bi-continuity: the bijection from the space of clouds to the space of
complete invariants is Lipschitz continuous in both directions in a
suitable metric d on the invariant space; (d) Computability: the
invariant I, the metric d, and a reconstruction of A in R^n from I(A)
can be obtained in polynomial time in the size of A, for a fixed
dimension n. The talk will outline a full solution to this problem,
which remains open for other data (embedded graphs, meshes, or
complexes) and relations (dilation, affine, or projective maps).
[1] V.Kurlin. Complete and continuous invariants of 1-periodic
sequences in polynomial time. SIAM Journal of Mathematics of Data
Science, 2025.
[2] P.Smith, V.Kurlin. Generic families of finite metric spaces with
identical or trivial 1-dimensional persistence. J Applied and
Computational Topology, v.8, p.839-855 (2024).
[3] D.Widdowson, V.Kurlin. Recognizing rigid patterns of unlabeled
point clouds by complete and continuous isometry invariants with no
false negatives and no false positives. Proceedings of CVPR 2023,
p.1275-1284.