333 research outputs found
On the Reconstruction of Geodesic Subspaces of
We consider the topological and geometric reconstruction of a geodesic
subspace of both from the \v{C}ech and Vietoris-Rips filtrations
on a finite, Hausdorff-close, Euclidean sample. Our reconstruction technique
leverages the intrinsic length metric induced by the geodesics on the subspace.
We consider the distortion and convexity radius as our sampling parameters for
a successful reconstruction. For a geodesic subspace with finite distortion and
positive convexity radius, we guarantee a correct computation of its homotopy
and homology groups from the sample. For geodesic subspaces of ,
we also devise an algorithm to output a homotopy equivalent geometric complex
that has a very small Hausdorff distance to the unknown shape of interest
On Vietoris-Rips complexes of ellipses
For a metric space and a scale parameter, the Vietoris-Rips complex
(resp. ) has as its vertex set, and a finite
subset as a simplex whenever the diameter of is
less than (resp. at most ). Though Vietoris-Rips complexes have been
studied at small choices of scale by Hausmann and Latschev, they are not
well-understood at larger scale parameters. In this paper we investigate the
homotopy types of Vietoris-Rips complexes of ellipses of small eccentricity, meaning
. Indeed, we show there are constants such that for
all , we have and , though only one of the two-spheres in is
persistent. Furthermore, we show that for any scale parameter ,
there are arbitrarily dense subsets of the ellipse such that the Vietoris-Rips
complex of the subset is not homotopy equivalent to the Vietoris-Rips complex
of the entire ellipse. As our main tool we link these homotopy types to the
structure of infinite cyclic graphs
Topological Data Analysis with Bregman Divergences
Given a finite set in a metric space, the topological analysis generalizes
hierarchical clustering using a 1-parameter family of homology groups to
quantify connectivity in all dimensions. The connectivity is compactly
described by the persistence diagram. One limitation of the current framework
is the reliance on metric distances, whereas in many practical applications
objects are compared by non-metric dissimilarity measures. Examples are the
Kullback-Leibler divergence, which is commonly used for comparing text and
images, and the Itakura-Saito divergence, popular for speech and sound. These
are two members of the broad family of dissimilarities called Bregman
divergences.
We show that the framework of topological data analysis can be extended to
general Bregman divergences, widening the scope of possible applications. In
particular, we prove that appropriately generalized Cech and Delaunay (alpha)
complexes capture the correct homotopy type, namely that of the corresponding
union of Bregman balls. Consequently, their filtrations give the correct
persistence diagram, namely the one generated by the uniformly growing Bregman
balls. Moreover, we show that unlike the metric setting, the filtration of
Vietoris-Rips complexes may fail to approximate the persistence diagram. We
propose algorithms to compute the thus generalized Cech, Vietoris-Rips and
Delaunay complexes and experimentally test their efficiency. Lastly, we explain
their surprisingly good performance by making a connection with discrete Morse
theory
Random geometric complexes
We study the expected topological properties of Cech and Vietoris-Rips
complexes built on i.i.d. random points in R^d. We find higher dimensional
analogues of known results for connectivity and component counts for random
geometric graphs. However, higher homology H_k is not monotone when k > 0. In
particular for every k > 0 we exhibit two thresholds, one where homology passes
from vanishing to nonvanishing, and another where it passes back to vanishing.
We give asymptotic formulas for the expectation of the Betti numbers in the
sparser regimes, and bounds in the denser regimes. The main technical
contribution of the article is in the application of discrete Morse theory in
geometric probability.Comment: 26 pages, 3 figures, final revisions, to appear in Discrete &
Computational Geometr
Approximating Loops in a Shortest Homology Basis from Point Data
Inference of topological and geometric attributes of a hidden manifold from
its point data is a fundamental problem arising in many scientific studies and
engineering applications. In this paper we present an algorithm to compute a
set of loops from a point data that presumably sample a smooth manifold
. These loops approximate a {\em shortest} basis of the
one dimensional homology group over coefficients in finite field
. Previous results addressed the issue of computing the rank of
the homology groups from point data, but there is no result on approximating
the shortest basis of a manifold from its point sample. In arriving our result,
we also present a polynomial time algorithm for computing a shortest basis of
for any finite {\em simplicial complex} whose edges have
non-negative weights
Nerve complexes of circular arcs
We show that the nerve complex of n arcs in the circle is homotopy equivalent
to either a point, an odd-dimensional sphere, or a wedge sum of spheres of the
same even dimension. Moreover this homotopy type can be computed in time O(n
log n). For the particular case of the nerve complex of evenly-spaced arcs of
the same length, we determine the dihedral group action on homology, and we
relate the complex to a cyclic polytope with n vertices. We give three
applications of our knowledge of the homotopy types of nerve complexes of
circular arcs. First, we use the connection to cyclic polytopes to give a novel
topological proof of a known upper bound on the distance between successive
roots of a homogeneous trigonometric polynomial. Second, we show that the
Lovasz bound on the chromatic number of a circular complete graph is either
sharp or off by one. Third, we show that the Vietoris--Rips simplicial complex
of n points in the circle is homotopy equivalent to either a point, an
odd-dimensional sphere, or a wedge sum of spheres of the same even dimension,
and furthermore this homotopy type can be computed in time O(n log n)
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