38,515 research outputs found
Graded persistence diagrams and persistence landscapes
We introduce a refinement of the persistence diagram, the graded persistence
diagram. It is the Mobius inversion of the graded rank function, which is
obtained from the rank function using the unary numeral system. Both
persistence diagrams and graded persistence diagrams are integer-valued
functions on the Cartesian plane. Whereas the persistence diagram takes
non-negative values, the graded persistence diagram takes values of 0, 1, or
-1. The sum of the graded persistence diagrams is the persistence diagram. We
show that the positive and negative points in the k-th graded persistence
diagram correspond to the local maxima and minima, respectively, of the k-th
persistence landscape. We prove a stability theorem for graded persistence
diagrams: the 1-Wasserstein distance between k-th graded persistence diagrams
is bounded by twice the 1-Wasserstein distance between the corresponding
persistence diagrams, and this bound is attained. In the other direction, the
1-Wasserstein distance is a lower bound for the sum of the 1-Wasserstein
distances between the k-th graded persistence diagrams. In fact, the
1-Wasserstein distance for graded persistence diagrams is more discriminative
than the 1-Wasserstein distance for the corresponding persistence diagrams.Comment: accepted for publication in Discrete and Computational Geometr
Categorification of persistent homology
We redevelop persistent homology (topological persistence) from a categorical
point of view. The main objects of study are diagrams, indexed by the poset of
real numbers, in some target category. The set of such diagrams has an
interleaving distance, which we show generalizes the previously-studied
bottleneck distance. To illustrate the utility of this approach, we greatly
generalize previous stability results for persistence, extended persistence,
and kernel, image and cokernel persistence. We give a natural construction of a
category of interleavings of these diagrams, and show that if the target
category is abelian, so is this category of interleavings.Comment: 27 pages, v3: minor changes, to appear in Discrete & Computational
Geometr
A Comparison Framework for Interleaved Persistence Modules
We present a generalization of the induced matching theorem and use it to
prove a generalization of the algebraic stability theorem for
-indexed pointwise finite-dimensional persistence modules. Via
numerous examples, we show how the generalized algebraic stability theorem
enables the computation of rigorous error bounds in the space of persistence
diagrams that go beyond the typical formulation in terms of bottleneck (or log
bottleneck) distance
Wasserstein Stability for Persistence Diagrams
The stability of persistence diagrams is among the most important results in
applied and computational topology. Most results in the literature phrase
stability in terms of the bottleneck distance between diagrams and the
-norm of perturbations. This has two main implications: it makes the
space of persistence diagrams rather pathological and it is often provides very
pessimistic bounds with respect to outliers. In this paper, we provide new
stability results with respect to the -Wasserstein distance between
persistence diagrams. This includes an elementary proof for the setting of
functions on sufficiently finite spaces in terms of the -norm of the
perturbations, along with an algebraic framework for -Wasserstein distance
which extends the results to wider class of modules. We also provide apply the
results to a wide range of applications in topological data analysis (TDA)
including topological summaries, persistence transforms and the special but
important case of Vietoris-Rips complexes
On the Metric Distortion of Embedding Persistence Diagrams into Separable Hilbert Spaces
Persistence diagrams are important descriptors in Topological Data Analysis. Due to the nonlinearity of the space of persistence diagrams equipped with their diagram distances, most of the recent attempts at using persistence diagrams in machine learning have been done through kernel methods, i.e., embeddings of persistence diagrams into Reproducing Kernel Hilbert Spaces, in which all computations can be performed easily. Since persistence diagrams enjoy theoretical stability guarantees for the diagram distances, the metric properties of the feature map, i.e., the relationship between the Hilbert distance and the diagram distances, are of central interest for understanding if the persistence diagram guarantees carry over to the embedding. In this article, we study the possibility of embedding persistence diagrams into separable Hilbert spaces with bi-Lipschitz maps. In particular, we show that for several stable embeddings into infinite-dimensional Hilbert spaces defined in the literature, any lower bound must depend on the cardinalities of the persistence diagrams, and that when the Hilbert space is finite dimensional, finding a bi-Lipschitz embedding is impossible, even when restricting the persistence diagrams to have bounded cardinalities
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