A Higher-Dimensional Homologically Persistent Skeleton
- Submitting institution
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The University of Liverpool
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12136
- Type
- D - Journal article
- DOI
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10.1016/j.aam.2018.07.004
- Title of journal
- Advances in Applied Mathematics
- Article number
- -
- First page
- 113
- Volume
- 102
- Issue
- January 2019
- ISSN
- 0196-8858
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper extends the techniques introduced by Kurlin in "A one‐dimensional homologically persistent skeleton of an unstructured point cloud in any metric space" (Computer Graphics Forum 2015; not REF returned) from the one-dimensional to the higher-dimensional case. According to the paper "Topological analysis of data" by Patania et al. (2017) this contribution has "[made] topological features amenable to network techniques".
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -