Difference between revisions of "Introductory reading"
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| − | This is a list of entries to the literature. | + | This is a list of entries to the vastly growing literature in the field of topological data analysis, both from a theoretical and an applied point of view. |
== Literature for Mathematicians == | == Literature for Mathematicians == | ||
| + | A well-known theoretical introduction to the field. Carlsson: ''Topology and Data''. <ref>CARLSSON, Gunnar. Topology and data. ''Bulletin of the American Mathematical Society'', 2009, 46. Jg., Nr. 2, S. 255-308. [https://doi.org/10.1090/S0273-0979-09-01249-X doi:10.1090/S0273-0979-09-01249-X]</ref> | ||
| + | One of the papers introducing the notion of persistent homology of a filtration. Zomorodian and Carlsson: ''Computing persistent homology''.<ref>ZOMORODIAN, Afra; CARLSSON, Gunnar. Computing persistent homology. ''Discrete & Computational Geometry'', 2005, 33. Jg., Nr. 2, S. 249-274. [https://doi.org/10.1007/s00454-004-1146-y doi:10.1007/s00454-004-1146-y]</ref> | ||
| + | A recently introduced topological summary better suited than persistence diagrams to statistically analyze persistent homology, the persistence landscape. Bubenik: ''Statistical topological data analysis using persistence landscapes.''<ref>BUBENIK, Peter. Statistical topological data analysis using persistence landscapes. ''The Journal of Machine Learning Research'', 2015, 16. Jg., Nr. 1, S. 77-102. http://jmlr.org/papers/v16/bubenik15a.html</ref> | ||
== Literature for Computer Scientists == | == Literature for Computer Scientists == | ||
| − | Edelsbrunner | + | A thorough introduction to the field and approaches of computational topology. Edelsbrunner and Harer: ''Computational topology: an introduction''.<ref>H. Edelsbrunner and J. Harer, ''Computational topology: an introduction''. Providence, R.I: American Mathematical Society, 2010. |
| − | <ref>H. Edelsbrunner and J. Harer, ''Computational topology: an introduction''. Providence, R.I: American Mathematical Society, 2010. | ||
<span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=urn%3Aisbn%3A978-0-8218-4925-5&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Computational%20topology%3A%20an%20introduction&rft.place=Providence%2C%20R.I&rft.publisher=American%20Mathematical%20Society&rft.aufirst=Herbert&rft.aulast=Edelsbrunner&rft.au=Herbert%20Edelsbrunner&rft.au=J.%20Harer&rft.date=2010&rft.tpages=241&rft.isbn=978-0-8218-4925-5"></span> | <span class="Z3988" title="url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fzotero.org%3A2&rft_id=urn%3Aisbn%3A978-0-8218-4925-5&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=book&rft.btitle=Computational%20topology%3A%20an%20introduction&rft.place=Providence%2C%20R.I&rft.publisher=American%20Mathematical%20Society&rft.aufirst=Herbert&rft.aulast=Edelsbrunner&rft.au=Herbert%20Edelsbrunner&rft.au=J.%20Harer&rft.date=2010&rft.tpages=241&rft.isbn=978-0-8218-4925-5"></span> | ||
</ref> | </ref> | ||
| + | |||
| + | A general overview of persistent homology with an eye towards applications of different numerical libraries. In the supplementaries starting points for own numerical steps are given. Otter et al: ''A roadmap for the computation of persistent homology''.<ref>OTTER, Nina, et al. A roadmap for the computation of persistent homology. ''EPJ Data Science'', 2017, 6. Jg., Nr. 1, S. 17. [https://doi.org/10.1140/epjds/s13688-017-0109-5 doi:10.1140/epjds/s13688-017-0109-5]</ref> | ||
| + | |||
| + | The mapper algorithm. Singh et al: ''Topological methods for the analysis of high dimensional data sets and 3d object recognition.''<ref>SINGH, Gurjeet; MÉMOLI, Facundo; CARLSSON, Gunnar E. Topological methods for the analysis of high dimensional data sets and 3d object recognition. In: ''SPBG''. 2007. S. 91-100. [http://dx.doi.org/10.2312/SPBG/SPBG07/091-100 doi:10.2312/SPBG/SPBG07/091-100]</ref> | ||
== Literature for Physicists == | == Literature for Physicists == | ||
Revision as of 20:53, 5 May 2019
This is a list of entries to the vastly growing literature in the field of topological data analysis, both from a theoretical and an applied point of view.
Literature for Mathematicians
A well-known theoretical introduction to the field. Carlsson: Topology and Data. [1]
One of the papers introducing the notion of persistent homology of a filtration. Zomorodian and Carlsson: Computing persistent homology.[2] A recently introduced topological summary better suited than persistence diagrams to statistically analyze persistent homology, the persistence landscape. Bubenik: Statistical topological data analysis using persistence landscapes.[3]
Literature for Computer Scientists
A thorough introduction to the field and approaches of computational topology. Edelsbrunner and Harer: Computational topology: an introduction.[4]
A general overview of persistent homology with an eye towards applications of different numerical libraries. In the supplementaries starting points for own numerical steps are given. Otter et al: A roadmap for the computation of persistent homology.[5]
The mapper algorithm. Singh et al: Topological methods for the analysis of high dimensional data sets and 3d object recognition.[6]
Literature for Physicists
Literature for Biologists
References
- ↑ CARLSSON, Gunnar. Topology and data. Bulletin of the American Mathematical Society, 2009, 46. Jg., Nr. 2, S. 255-308. doi:10.1090/S0273-0979-09-01249-X
- ↑ ZOMORODIAN, Afra; CARLSSON, Gunnar. Computing persistent homology. Discrete & Computational Geometry, 2005, 33. Jg., Nr. 2, S. 249-274. doi:10.1007/s00454-004-1146-y
- ↑ BUBENIK, Peter. Statistical topological data analysis using persistence landscapes. The Journal of Machine Learning Research, 2015, 16. Jg., Nr. 1, S. 77-102. http://jmlr.org/papers/v16/bubenik15a.html
- ↑ H. Edelsbrunner and J. Harer, Computational topology: an introduction. Providence, R.I: American Mathematical Society, 2010.
- ↑ OTTER, Nina, et al. A roadmap for the computation of persistent homology. EPJ Data Science, 2017, 6. Jg., Nr. 1, S. 17. doi:10.1140/epjds/s13688-017-0109-5
- ↑ SINGH, Gurjeet; MÉMOLI, Facundo; CARLSSON, Gunnar E. Topological methods for the analysis of high dimensional data sets and 3d object recognition. In: SPBG. 2007. S. 91-100. doi:10.2312/SPBG/SPBG07/091-100