Section: New Results
Topological and Geometric Inference
Homological reconstruction and simplification in
Participants : Olivier Devillers, Marc Glisse.
In collaboration with Dominique Attali (Gipsa-lab), Ulrich Bauer (Göttingen Univ.), and André Lieutier (Dassault Systèmes).
We consider the problem of deciding whether the persistent homology group
of a simplicial pair
As a consequence, we show that it is NP-hard to simplify level and
sublevel sets of scalar functions on
The structure and stability of persistence modules
Participants : Frédéric Chazal, Marc Glisse, Steve Oudot.
In collaboration with Vin de Silva (Pomona College)
We give a self-contained treatment of the theory of persistence modules indexed over the real line. We give new proofs of the standard results. Persistence diagrams are constructed using measure theory. Linear algebra lemmas are simplified using a new notation for calculations on quiver representations. We show that the stringent finiteness conditions required by traditional methods are not necessary to prove the existence and stability of the persistence diagram. We introduce weaker hypotheses for taming persistence modules, which are met in practice and are strong enough for the theory still to work. The constructions and proofs enabled by our framework are, we claim, cleaner and simpler [54] .
Persistence stability for geometric complexes
Participants : Frédéric Chazal, Steve Oudot.
In collaboration with Vin de Silva (Pomona College)
We study the properties of the homology of different geometric filtered complexes (such as Vietoris–Rips, Čech and witness complexes) built on top of precompact spaces. Using recent developments in the theory of topological persistence [54] we provide simple and natural proofs of the stability of the persistent homology of such complexes with respect to the Gromov–Hausdorff distance. We also exhibit a few noteworthy properties of the homology of the Rips and Čech complexes built on top of compact spaces [53] .
Zigzag zoology: rips zigzags for homology inference
Participants : Steve Oudot, Donald Sheehy.
For points sampled near a compact set
Along the way, we develop new techniques for manipulating and comparing persistence barcodes from zigzag modules. We give methods for reversing arrows and removing spaces from a zigzag. We also discuss factoring zigzags and a kind of interleaving of two zigzags that allows their barcodes to be compared. These techniques were developed to provide our theoretical analysis of the signal-to-noise ratio of Rips-like zigzags, but they are of independent interest as they apply to zigzag modules generally [60] .
A space and time efficient implementation for computing persistent homology
Participants : Jean-Daniel Boissonnat, Clément Maria.
In collaboration with Tamal Dey (Ohio State University)
The persistent homology with
Minimax rates for homology inference
Participant : Donald Sheehy.
In collaboration with Sivaraman Balakrishnan and Alessandro Rinaldo and Aarti Singh and Larry A. Wasserman (Carnegie Mellon University)
Often, high dimensional data lie close to a low-dimensional submanifold and it is of interest to understand the geometry of these submanifolds. The homology groups of a manifold are important topological invariants that provide an algebraic summary of the manifold. These groups contain rich topological information, for instance, about the connected components, holes, tunnels and sometimes the dimension of the manifold. We consider the statistical problem of estimating the homology of a manifold from noisy samples under several different noise models. We derive upper and lower bounds on the minimax risk for this problem. Our upper bounds are based on estimators which are constructed from a union of balls of appropriate radius around carefully selected points. In each case, we establish complementary lower bounds using Le Cam's lemma [15] .
Linear-size approximations to the Vietoris-Rips filtration
Participant : Donald Sheehy.
The Vietoris-Rips filtration is a versatile tool in topological data analysis. Unfortunately, it is often too large to construct in full. We show how to construct an
A multicover nerve for geometric inference
Participant : Donald Sheehy.
We show that filtering the barycentric decomposition of a Čech complex by the cardinality of the vertices captures precisely the topology of
Computing well diagrams for vector fields on
Participant : Frédéric Chazal.
In collaboration with Primoz Skraba (Lubiana Univ.), Amit Patel (Rutgers Univ.)
Using topological degree theory, we present and prove correctness of a fast algorithm for computing the well diagram, a quantitative property, of a vector field on Euclidean space [17] .