- See homology for an introduction to the notation.
Persistent homology is a method for computing topological features of a space at different spatial resolutions. More persistent features are detected over a wide range of length and are deemed more likely to represent true features of the underlying space, rather than artifacts of sampling, noise, or particular choice of parameters.
To find the persistent homology of a space, the space must first be represented as a simplicial complex. A distance function on the underlying space corresponds to a filtration of the simplicial complex, that is a nested sequence of increasing subsets.
Formally, consider a real-valued function on a simplicial complex that is non-decreasing on increasing sequences of faces, so whenever is a face of in . Then for every the sublevel set is a subcomplex of K, and the ordering of the values of on the simplices in (which is in practice always finite) induces an ordering on the sublevel complexes that defines a filtration
When , the inclusion induces a homomorphism on the simplicial homology groups for each dimension . The persistent homology groups are the images of these homomorphisms, and the persistent Betti numbers are the ranks of those groups. Persistent Betti numbers for coincide with the size function, a predecessor of persistent homology.
A persistence module over a partially ordered set is a set of vector spaces indexed by , with a linear map whenever , with equal to the identity and for . Equivalently, we may consider it as a functor from considered as a category to the category of vector spaces (or -modules). There is a classification of persistence modules over a field indexed by :
This theorem allows us to uniquely represent the persistent homology of a filtered simplicial complex with a barcode or persistence diagram. A barcode represents each persistent generator with a horizontal line beginning at the first filtration level where it appears, and ending at the filtration level where it disappears, while a persistence diagram plots a point for each generator with its x-coordinate the birth time and its y-coordinate the death time.
Persistent homology is stable in a precise sense, which provides robustness against noise. There is a natural metric on the space of persistence diagrams given by
There are various software packages for computing persistence intervals of a finite filtration.
|Software package||Creator||Latest release||Release date||Software license||Open source||Programming language||Features|
|javaPlex||Andrew Tausz, Mikael Vejdemo-Johansson, Henry Adams||4.2.5||14 March 2016||Custom||Yes||Java, Matlab|
|Dionysus||Dmitriy Morozov||GPL||Yes||C++, Python bindings|
|Perseus||Vidit Nanda||4.0 beta||GPL||Yes||C++|
|PHAT||Ulrich Bauer, Michael Kerber, Jan Reininghaus||1.4.1||Yes||C++|
|Gudhi||INRIA||1.3.0||15 April 2016||Yes||C++|
|TDA||Brittany T. Fasy, Jisu Kim, Fabrizio Lecci, Clement Maria, Vincent Rouvreau||1.5||16 June 2016||Yes||R|
|Ripser||Ulrich Bauer||1.0.1||15 September 2016||LGPL||Yes||C++|
|Software package||Creator||Latest stable release||Stable release date||Software license||Open source||Programming language|
- Carlsson, Gunnar (2009). "Topology and data". AMS Bulletin 46(2), 255–308.
- Edelsbrunner, H and Harer, J (2010). Computational Topology: An Introduction. American Mathematical Society.
- Verri, A, Uras, C, Frosini, P and Ferri, M (1993). On the use of size functions for shape analysis, Biological Cybernetics, 70, 99–107.
- Zomorodian, Afra; Carlsson, Gunnar (2004-11-19). "Computing Persistent Homology". Discrete & Computational Geometry. 33 (2): 249–274. ISSN 0179-5376. doi:10.1007/s00454-004-1146-y.
- Cohen-Steiner, David; Edelsbrunner, Herbert; Harer, John (2006-12-12). "Stability of Persistence Diagrams". Discrete & Computational Geometry. 37 (1): 103–120. ISSN 0179-5376. doi:10.1007/s00454-006-1276-5.
- Licenses here are a summary, and are not taken to be complete statements of the licenses. Some packages may use libraries under different licenses.