129 research outputs found
Approximating Persistent Homology in Euclidean Space Through Collapses
The \v{C}ech complex is one of the most widely used tools in applied
algebraic topology. Unfortunately, due to the inclusive nature of the \v{C}ech
filtration, the number of simplices grows exponentially in the number of input
points. A practical consequence is that computations may have to terminate at
smaller scales than what the application calls for.
In this paper we propose two methods to approximate the \v{C}ech persistence
module. Both are constructed on the level of spaces, i.e. as sequences of
simplicial complexes induced by nerves. We also show how the bottleneck
distance between such persistence modules can be understood by how tightly they
are sandwiched on the level of spaces. In turn, this implies the correctness of
our approximation methods.
Finally, we implement our methods and apply them to some example point clouds
in Euclidean space
Sparse Nerves in Practice
Topological data analysis combines machine learning with methods from
algebraic topology. Persistent homology, a method to characterize topological
features occurring in data at multiple scales is of particular interest. A
major obstacle to the wide-spread use of persistent homology is its
computational complexity. In order to be able to calculate persistent homology
of large datasets, a number of approximations can be applied in order to reduce
its complexity. We propose algorithms for calculation of approximate sparse
nerves for classes of Dowker dissimilarities including all finite Dowker
dissimilarities and Dowker dissimilarities whose homology is Cech persistent
homology. All other sparsification methods and software packages that we are
aware of calculate persistent homology with either an additive or a
multiplicative interleaving. In dowker_homology, we allow for any
non-decreasing interleaving function . We analyze the computational
complexity of the algorithms and present some benchmarks. For Euclidean data in
dimensions larger than three, the sizes of simplicial complexes we create are
in general smaller than the ones created by SimBa. Especially when calculating
persistent homology in higher homology dimensions, the differences can become
substantial
A Geometric Perspective on Sparse Filtrations
We present a geometric perspective on sparse filtrations used in topological
data analysis. This new perspective leads to much simpler proofs, while also
being more general, applying equally to Rips filtrations and Cech filtrations
for any convex metric. We also give an algorithm for finding the simplices in
such a filtration and prove that the vertex removal can be implemented as a
sequence of elementary edge collapses
Strong Collapse for Persistence
We introduce a fast and memory efficient approach to compute the persistent homology (PH) of a sequence of simplicial complexes. The basic idea is to simplify the complexes of the input sequence by using strong collapses, as introduced by J. Barmak and E. Miniam [DCG (2012)], and to compute the PH of an induced sequence of reduced simplicial complexes that has the same PH as the initial one. Our approach has several salient features that distinguishes it from previous work. It is not limited to filtrations (i.e. sequences of nested simplicial subcomplexes) but works for other types of sequences like towers and zigzags. To strong collapse a simplicial complex, we only need to store the maximal simplices of the complex, not the full set of all its simplices, which saves a lot of space and time. Moreover, the complexes in the sequence can be strong collapsed independently and in parallel. As a result and as demonstrated by numerous experiments on publicly available data sets, our approach is extremely fast and memory efficient in practice
Computing Persistent Homology of Flag Complexes via Strong Collapses
In this article, we focus on the problem of computing Persistent Homology of a flag tower, i.e. a sequence of flag complexes connected by simplicial maps. We show that if we restrict the class of simplicial complexes to flag complexes, we can achieve decisive improvement in terms of time and space complexities with respect to previous work. We show that strong collapses of flag complexes can be computed in time O(k^2v^2) where v is the number of vertices of the complex and k is the maximal degree of its graph. Moreover we can strong collapse a flag complex knowing only its 1-skeleton and the resulting complex is also a flag complex. When we strong collapse the complexes in a flag tower, we obtain a reduced sequence that is also a flag tower we call the core flag tower. We then convert the core flag tower to an equivalent filtration to compute its PH. Here again, we only use the 1-skeletons of the complexes. The resulting method is simple and extremely efficient
Edge Collapse and Persistence of Flag Complexes
In this article, we extend the notions of dominated vertex and strong collapse of a simplicial complex as introduced by J. Barmak and E. Miniam. We say that a simplex (of any dimension) is dominated if its link is a simplicial cone. Domination of edges appears to be a very powerful concept, especially when applied to flag complexes. We show that edge collapse (removal of dominated edges) in a flag complex can be performed using only the 1-skeleton of the complex. Furthermore, the residual complex is a flag complex as well. Next we show that, similar to the case of strong collapses, we can use edge collapses to reduce a flag filtration ? to a smaller flag filtration ?^c with the same persistence. Here again, we only use the 1-skeletons of the complexes. The resulting method to compute ?^c is simple and extremely efficient and, when used as a preprocessing for persistence computation, leads to gains of several orders of magnitude w.r.t the state-of-the-art methods (including our previous approach using strong collapse). The method is exact, irrespective of dimension, and improves performance of persistence computation even in low dimensions. This is demonstrated by numerous experiments on publicly available data
Polynomial-Sized Topological Approximations Using The Permutahedron
Classical methods to model topological properties of point clouds, such as
the Vietoris-Rips complex, suffer from the combinatorial explosion of complex
sizes. We propose a novel technique to approximate a multi-scale filtration of
the Rips complex with improved bounds for size: precisely, for points in
, we obtain a -approximation with at most simplices of dimension or lower. In conjunction with dimension
reduction techniques, our approach yields a -approximation of size for Rips filtrations on arbitrary metric
spaces. This result stems from high-dimensional lattice geometry and exploits
properties of the permutahedral lattice, a well-studied structure in discrete
geometry.
Building on the same geometric concept, we also present a lower bound result
on the size of an approximate filtration: we construct a point set for which
every -approximation of the \v{C}ech filtration has to contain
features, provided that for .Comment: 24 pages, 1 figur
Sparse Nerves in Practice
Topological data analysis combines machine learning with methods from algebraic topology. Persistent homology, a method to characterize topological features occurring in data at multiple scales is of particular interest. A major obstacle to the wide-spread use of persistent homology is its computational complexity. In order to be able to calculate persistent homology of large datasets, a number of approximations can be applied in order to reduce its complexity. We propose algorithms for calculation of approximate sparse nerves for classes of Dowker dissimilarities including all finite Dowker dissimilarities and Dowker dissimilarities whose homology is Čech persistent homology. All other sparsification methods and software packages that we are aware of calculate persistent homology with either an additive or a multiplicative interleaving. In dowker_homology, we allow for any non-decreasing interleaving function α . We analyze the computational complexity of the algorithms and present some benchmarks. For Euclidean data in dimensions larger than three, the sizes of simplicial complexes we create are in general smaller than the ones created by SimBa. Especially when calculating persistent homology in higher homology dimensions, the differences can become substantial.acceptedVersio
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