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Persistent Homology for Kernels, Images, and Cokernels ∗

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Persistent Homology for Kernels, Images, and Cokernels ∗ Persistent Homology for Kernels, Images, and Cokernels ∗ David Cohen-Steiner†, Herbert Edelsbrunner‡, John Harer§ and Dmitriy Morozov¶ Abstract tent homology arises from considering the corresponding se- quence of homology groups, H(X0) → H(X1) → . → Motivated by the measurement of local homology and of H(Xm), connected from left to right by homomorphic maps functions on noisy domains, we extend the notion of persis- induced by inclusion. Persistence tracks when a homology tent homology to sequences of kernels, images, and coker- class is born and when it dies. This can also be done for nels of maps induced by inclusions in a filtration of pairs of an arbitrary sequence of vector spaces connected by homo- spaces. Specifically, we note that persistence in this context morphic maps. Motivation for studying such more general is well defined, we prove that the persistence diagrams are sequences is derived from recent investigations in computa- stable, and we explain how to compute them. tional topology. First, Bendich et al. describe a multi-scale Keywords. Persistent homology, kernels, images, cokernels, per- assessment of local homology for the purpose of reconstruct- sistence diagrams, vineyards, algorithms, stratified spaces. ing a stratified space from a point sample [2]. We will see how sequences of kernels can be used to refine their con- struction. Second, we use sequences of images to introduce 1 Introduction a notion of persistence that filters out noise induced by im- precise specifications of domains. This contrasts standard Natural phenomena are often modeled in terms of spaces and persistence which can handle imprecise function values but functions on these spaces. We argue that it is almost always not imprecise domains. As an application, we will approxi- more appropriate to use a multi-scale hierarchy instead of a mate the persistence diagram of a function knowing only its single space. One reason is the prevalent multi-scale orga- values at a finite set of points. The main contributions of this nization we find in nature, another is that the data we gather paper are two-fold: about nature is necessarily incomplete and requires interpo- lation. The multi-scale aspect helps bridging the gap be- tween the data about natural phenomena and the idealized • an algorithm that computes the persistence diagrams of mathematical concepts we use for exploration. In particu- sequences of kernels, images, and cokernels in time at lar, we consider homologygroups, which are algebraic struc- most cubic in the size of the simplicial complexes rep- tures that define and count holes in a topological space [11]. resenting the data; Their multi-scale extensions are persistent homology groups introduced in [10, 13]. Similar to homology which not only • applications of the algebraic and algorithmic results to counts but also defines, persistent homology not only mea- measuring local homology and to approximating persis- sures but also creates the hierarchy. tence diagrams of noisy functions on noisy domains. In all previoussettings, the hierarchyis defined by a nested sequence of spaces, X0 ⊆ X1 ⊆ . ⊆ Xm, and persis- ∗This research is partially supported by the Defense Advanced Research Projects Agency (DARPA) under grants HR0011-05-1-0007 and HR0011- Outline. The remainder of this paper is structured as fol- 05-1-0057 and by CNRS under grant PICS-3416. lows. Section 2 introduces the algebra of persistent homol- †INRIA, 2004 Route des Lucioles, BP93, Sophia-Antipolis, France. ‡Departments of Computer Science and of Mathematics, Duke Uni- ogy including its extension to sequences of kernels, images versity, Durham, Berlin Mathematical School, Berlin, Germany, and Ge- and cokernels. Section 3 explains the algorithms for com- omagic, Research Triangle Park, North Carolina, USA. puting the corresponding persistence diagrams for a nested § Departments of Mathematics and of Computer Science, Duke Univer- sequence of pairs of spaces and Section 4 proves their cor- sity, Durham, North Carolina, USA. ¶Department of Computer Science, Duke University, Durham, North rectness. Section 5 presents the two applications of our alge- Carolina, USA. braic and algorithmic results. Section 6 concludes the paper. 2 Algebra two-dimensional plane. By collecting the points for all p- dimensional classes we get the dimension p persistence dia- Beginning with a review of persistent homology, this section gram which we denote as Dgmp(p). Since birth necessarily extends this concept to sequences of kernels, images, and happens before death all points lie above the diagonal. It cokernels. It also proves that the persistence diagrams of is also possible that a class γ is born at ai but does not die these extensions are stable. since it represents a class of Xm = X. In this case, we draw γ as the point (ai, ∞) in the diagram. For technical reasons that will become clear later, we consider all points on the Persistent homology. This concept is a recent addition to diagonal to be part of the persistence diagram. Similar to classical homology theory and was originally introduced for homology groups we get a diagram for each dimension and ordered simplicial complexes [10]. We follow the exposition we write Dgm(f) for the infinite series of diagrams. Same in [6] in which we have a topological space X and a continu- as for homology groups and for the maps between them we ous function f : X → R. The sublevel set defined by a ∈ R will simplify language by ignoring the difference between a consists of all points with function value at most the thresh- −1 single diagram and an entire series. old, Xa = f (−∞,a]. We use the algebraic language of homology theory to characterize how Xa is connected, see e.g. [11]. Adding chains with modulo-2 arithmetic, we write Hp(Xa) for the dimension p homology group over Z/2Z of Kernels, images, and cokernels. For the extension of per- Xa and H(Xa) = (..., Hp(Xa), Hp+1(Xa),...) for the infi- sistence to kernels, images, and cokernels we consider two nite series obtained by collecting the groups for all dimen- functions, f : X → R and a majorizing function g : Y → R sions. Of course, only the groups for p between 0 and the defined on a subspace Y ⊆ X, that is, f(y) ≤ g(y) for all dimension of Xa are possibly non-trivial. To simplify lan- y ∈ Y ⊆ X. Assuming both functions are tame, we or- guage, we will often ignore the difference between a single der the collection of critical values of f and g and interleave homology group and the entire series. Given a ≤ b, the them with a sequence of real values si. The corresponding inclusion between the sublevel sets, Xa ⊆ Xb, induces a ho- sequences of sublevel sets give rise to two parallel sequences momorphism, f a,b : H(Xa) → H(Xb). For a = b this is an of homology groups, isomorphism and for a<b it may or may not be an isomor- R phism. A value a ∈ is a homological critical value of f if H(X0) → H(X1) → . → H(Xm) there is no sufficiently small ε > 0 for which fa−ε,a+ε is an isomorphism. We assume that f is tame, that is, it has only ↑ j0 ↑ j1 . ↑ jm finitely many critical values and every sublevel set has only finite rank homology groups. H(Y0) → H(Y1) → . → H(Ym), Let a1 < a2 <...<am be the critical values of f and −1 −1 consider an interleaved sequence si−1 < ai < si for all i. where Xi = f (−∞,si] and Yi = g (−∞,si]. The two X X X This gives a sequence of spaces, 0 ⊆ 1 ⊆ . ⊆ m = sequences are connected by homomorphisms ji : H(Yi) → X X X , where we simplify notation by writing i = si , and a H(Xi) induced by the inclusions Yi ⊆ Xi. We call this the corresponding sequence of homology groups connected by two function setting, in contrast to the more special one func- homomorphisms, tion setting in which g is the restriction of f to Y. Moreabout the relationship between the two settings later. We are inter- H X H X H X ( 0) → ( 1) → . → ( m). ested in the kernels, images, and cokernels of the connecting homomorphisms, Persistence concerns itself with the history of individual ho- mology classes within this sequence. Specifically, a class ker ji = {γ ∈ H(Yi) | ji(γ)=0 ∈ H(Xi)}; γ in H(Xi) is born at ai if it is not in the image of fi−1,i = f im ji = {ji(γ) ∈ H(Xi) | γ ∈ H(Yi)}; si−1,si . More precisely, an entire cosetis bornat ai. Further- more, if γ is born at ai we say it dies entering aj if fi,j−1(γ) cok ji = H(Xi)/im ji. is not contained in the image of fi−1,j−1 but fi,j (γ) is con- f f tained in the image of i−1,j . The images of the maps i,j are We note that the “coimage” of the map ji, in symbols referred to as persistent homology groups since they consist H(Yi)/ker ji, is isomorphic to the image of this map, and of all homology groups born at or before ai that live beyond therefore does not deserve any special attention. Figure 1 il- aj . Nothing about the definition of birth and death is spe- lustrates this construction for two contiguous spaces in both cific to homology groups. In other words, persistence makes sequences. The square defined by the four homology groups perfect sense for any sequence of vector spaces connected by commutes. It follows that the inclusion Yi ⊆ Yi+1 induces homomorphisms. a homomorphism ker ji → ker ji+1. Similarly, the inclu- It is convenient to represent the fact that γ is born at ai sion Xi ⊆ Xi+1 induces a homomorphism im ji → im ji+1 and dies entering aj by drawing the point (ai,aj ) in the and another homomorphism cok ji → cok ji+1.
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