2017.30: Persistent homology for low-complexity models
2017.30: Martin Lotz (2017) Persistent homology for low-complexity models.
This is the latest version of this eprint.
Full text available as:
|PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
We show that recent results on randomized dimension reduction schemes that exploit structural properties of data can be applied in the context of persistent homology. In the spirit of compressed sensing, the dimension reduction is determined by the Gaussian width of a structure associated to the data set, rather than its size. The Gaussian width also turns out to be useful for studying the complexity of other methods for approximating persistent homology.
|Item Type:||MIMS Preprint|
|Uncontrolled Keywords:||Persistent homology; Topological data analysis; randomized dimension reduction; Johnson-Lindenstrauss|
|Subjects:||MSC 2000 > 52 Convex and discrete geometry|
MSC 2000 > 60 Probability theory and stochastic processes
MSC 2000 > 65 Numerical analysis
MSC 2000 > 68 Computer science
|Deposited By:||Dr. Martin Lotz|
|Deposited On:||03 October 2017|
Available Versions of this Item
- Persistent homology for low-complexity models (deposited 03 October 2017) [Currently Displayed]