Difference between revisions of "Topological Methods in Data Analysis - Journal Club (Winter 2021/22)"

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|29.11.
 
|29.11.
 
|UMap and manifold learning
 
|UMap and manifold learning
|Leland McInnes, John Healy, James Melville (2018) <br> https://arxiv.org/abs/1802.03426
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|McInnes et al. <b> Journal of Open Source Software, 3(29), 861 (2018) </b><br> https://arxiv.org/abs/1802.03426
 
|Paul Snopov
 
|Paul Snopov
 
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Revision as of 11:02, 29 November 2021

In recent years there has been a somewhat surprising flow of ideas from the mathematical branch of topology towards applications in the natural sciences. The tale of Topological Data Analysis (TDA) has it, that these methods provide a highly flexible, nonparametric approach to data analysis. Indeed, there are by now several well-known mathematical results that make statements of this kind rigorous. However, successful examples of TDA often build on a deep intuition about the system in question and many aspects of topological methods in data analysis remain a field of active research.

The goal of this seminar is to offer a platform where we can learn about the TDA toolkit, discuss articles and new developments, and exchange ideas for the analysis of concrete datasets.

Coordinates and Organization

Time: Mondays 11h15 - 12h45
Zoom 830 4593 4507
Please get in touch with us to receive the password.

Organizers: Michael Bleher, Maximilian Schmahl, Daniel Spitz.
structures-hiwi@mathi.uni-heidelberg.de

Schedule

Date Topic Info Speaker Slides
25.10. Introduction and Organization Michael Bleher, Maximilian Schmahl, Daniel Spitz
01.11. Allerheiligen, holiday
08.11. Embedding of persistence diagrams Mitra A. Proc. Amer. Math. Soc. 149 (2021), 2693-2703
https://arxiv.org/abs/1905.09337
Atish Mitra
15.11. Applications of topological data analysis on DNA data Hahn H, Neitzel C, Kopečná O, Heermann DW, Falk M, Hausmann M. Cancers. 2021; 13(21):5561.
https://doi.org/10.3390/cancers13215561
Jonas Weidner
22.11. Compact representations of simplicial complexes and classification algorithms Rolando Kindelan Nuñez (2021)
https://arxiv.org/abs/2102.03709
Rolando Kindelan
29.11. UMap and manifold learning McInnes et al. Journal of Open Source Software, 3(29), 861 (2018)
https://arxiv.org/abs/1802.03426
Paul Snopov
06.12. Optimal embedding of manifolds into Euclidean spaces Ilja Sirajlovic
13.12.
20.12.
--- Christmas Break ---
10.01.
17.01. TBA Clemens Bannwart
24.01.
31.01.
07.02.