Method for computing topological features of a space at different spatial resolutions
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 spatial scales and are deemed more likely to represent true features of the underlying space rather than artifacts of sampling, noise, or particular choice of parameters.[1]
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. One common method of doing this is via taking the sublevel filtration of the distance to a point cloud, or equivalently, the offset filtration on the point cloud and taking its nerve in order to get the simplicial filtration known as Čech filtration.[2] A similar construction uses a nested sequence of Vietoris–Rips complexes known as the Vietoris–Rips filtration.[3]
^Carlsson, Gunnar (2009). "Topology and data". AMS Bulletin46(2), 255–308.
^Kerber, Michael; Sharathkumar, R. (2013). "Approximate Čech Complex in Low and High Dimensions". In Cai, Leizhen; Cheng, Siu-Wing; Lam, Tak-Wah (eds.). Algorithms and Computation. Lecture Notes in Computer Science. Vol. 8283. Berlin, Heidelberg: Springer. pp. 666–676. doi:10.1007/978-3-642-45030-3_62. ISBN 978-3-642-45030-3. S2CID 5770506.
^Dey, Tamal K.; Shi, Dayu; Wang, Yusu (2019-01-30). "SimBa: An Efficient Tool for Approximating Rips-filtration Persistence via Simplicial Batch Collapse". ACM Journal of Experimental Algorithmics. 24: 1.5:1–1.5:16. doi:10.1145/3284360. ISSN 1084-6654. S2CID 216028146.
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