A numerical measure of the instability of Mapper-type algorithms
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 68651064
- Type
- D - Journal article
- DOI
-
-
- Title of journal
- Journal of Machine Learning Research
- Article number
- 202
- First page
- 1
- Volume
- 21
- Issue
- -
- ISSN
- 1533-7928
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2020
- 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
-
3
- Research group(s)
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-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work resulted from an EPSRC project “Joining the Dots: From Data to Insight, EP/N014189/1 (https://tinyurl.com/r9gre6b), in which we aimed to develop theoretical understanding of machine learning algorithms, placed in several inter-disciplinary applications with Topological Data Analysis (TDA) as the mathematical foundation. The paper develops novel and rigorous analyses of stability of a family of clustering algorithms (Mapper) which have extensive industrial uptake (https://www.ayasdi.com/) .
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -