3,597 research outputs found
Sheaf-Theoretic Stratification Learning from Geometric and Topological Perspectives
In this paper, we investigate a sheaf-theoretic interpretation of
stratification learning from geometric and topological perspectives. Our main
result is the construction of stratification learning algorithms framed in
terms of a sheaf on a partially ordered set with the Alexandroff topology. We
prove that the resulting decomposition is the unique minimal stratification for
which the strata are homogeneous and the given sheaf is constructible. In
particular, when we choose to work with the local homology sheaf, our algorithm
gives an alternative to the local homology transfer algorithm given in Bendich
et al. (2012), and the cohomology stratification algorithm given in Nanda
(2017). Additionally, we give examples of stratifications based on the
geometric techniques of Breiding et al. (2018), illustrating how the
sheaf-theoretic approach can be used to study stratifications from both
topological and geometric perspectives. This approach also points toward future
applications of sheaf theory in the study of topological data analysis by
illustrating the utility of the language of sheaf theory in generalizing
existing algorithms
Sheaf-Theoretic Stratification Learning
In this paper, we investigate a sheaf-theoretic interpretation of stratification learning. Motivated by the work of Alexandroff (1937) and McCord (1978), we aim to redirect efforts in the computational topology of triangulated compact polyhedra to the much more computable realm of sheaves on partially ordered sets. Our main result is the construction of stratification learning algorithms framed in terms of a sheaf on a partially ordered set with the Alexandroff topology. We prove that the resulting decomposition is the unique minimal stratification for which the strata are homogeneous and the given sheaf is constructible. In particular, when we choose to work with the local homology sheaf, our algorithm gives an alternative to the local homology transfer algorithm given in Bendich et al. (2012), and the cohomology stratification algorithm given in Nanda (2017). We envision that our sheaf-theoretic algorithm could give rise to a larger class of stratification beyond homology-based stratification. This approach also points toward future applications of sheaf theory in the study of topological data analysis by illustrating the utility of the language of sheaf theory in generalizing existing algorithms
Approximating Local Homology from Samples
Recently, multi-scale notions of local homology (a variant of persistent
homology) have been used to study the local structure of spaces around a given
point from a point cloud sample. Current reconstruction guarantees rely on
constructing embedded complexes which become difficult in high dimensions. We
show that the persistence diagrams used for estimating local homology, can be
approximated using families of Vietoris-Rips complexes, whose simple
constructions are robust in any dimension. To the best of our knowledge, our
results, for the first time, make applications based on local homology, such as
stratification learning, feasible in high dimensions.Comment: 23 pages, 14 figure
Local cohomology and stratification
We outline an algorithm to recover the canonical (or, coarsest)
stratification of a given finite-dimensional regular CW complex into cohomology
manifolds, each of which is a union of cells. The construction proceeds by
iteratively localizing the poset of cells about a family of subposets; these
subposets are in turn determined by a collection of cosheaves which capture
variations in cohomology of cellular neighborhoods across the underlying
complex. The result is a nested sequence of categories, each containing all the
cells as its set of objects, with the property that two cells are isomorphic in
the last category if and only if they lie in the same canonical stratum. The
entire process is amenable to efficient distributed computation.Comment: Final version, published in Foundations of Computational Mathematic
Dimension Detection with Local Homology
Detecting the dimension of a hidden manifold from a point sample has become
an important problem in the current data-driven era. Indeed, estimating the
shape dimension is often the first step in studying the processes or phenomena
associated to the data. Among the many dimension detection algorithms proposed
in various fields, a few can provide theoretical guarantee on the correctness
of the estimated dimension. However, the correctness usually requires certain
regularity of the input: the input points are either uniformly randomly sampled
in a statistical setting, or they form the so-called
-sample which can be neither too dense nor too sparse.
Here, we propose a purely topological technique to detect dimensions. Our
algorithm is provably correct and works under a more relaxed sampling
condition: we do not require uniformity, and we also allow Hausdorff noise. Our
approach detects dimension by determining local homology. The computation of
this topological structure is much less sensitive to the local distribution of
points, which leads to the relaxation of the sampling conditions. Furthermore,
by leveraging various developments in computational topology, we show that this
local homology at a point can be computed \emph{exactly} for manifolds
using Vietoris-Rips complexes whose vertices are confined within a local
neighborhood of . We implement our algorithm and demonstrate the accuracy
and robustness of our method using both synthetic and real data sets
Persistent Intersection Homology for the Analysis of Discrete Data
Topological data analysis is becoming increasingly relevant to support the
analysis of unstructured data sets. A common assumption in data analysis is
that the data set is a sample---not necessarily a uniform one---of some
high-dimensional manifold. In such cases, persistent homology can be
successfully employed to extract features, remove noise, and compare data sets.
The underlying problems in some application domains, however, turn out to
represent multiple manifolds with different dimensions. Algebraic topology
typically analyzes such problems using intersection homology, an extension of
homology that is capable of handling configurations with singularities. In this
paper, we describe how the persistent variant of intersection homology can be
used to assist data analysis in visualization. We point out potential pitfalls
in approximating data sets with singularities and give strategies for resolving
them.Comment: Topology-based Methods in Visualization 201
Stratified Space Learning: Reconstructing Embedded Graphs
Many data-rich industries are interested in the efficient discovery and
modelling of structures underlying large data sets, as it allows for the fast
triage and dimension reduction of large volumes of data embedded in high
dimensional spaces. The modelling of these underlying structures is also
beneficial for the creation of simulated data that better represents real data.
In particular, for systems testing in cases where the use of real data streams
might prove impractical or otherwise undesirable. We seek to discover and model
the structure by combining methods from topological data analysis with
numerical modelling. As a first step in combining these two areas, we examine
the recovery of the abstract graph structure, and model a linear embedding
given only a noisy point cloud sample of .Comment: 7 pages, 3 figures, accepted for MODSIM 2019 conferenc
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