The DOI matches its DataCite record; 18 of the 18 references it cites were found in a registry or an index.
Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition
Open accessAbstract
We present a computational method for extracting simple descriptions of high dimensional data sets in the form of simplicial complexes. Our method, called Mapper, is based on the idea of partial clustering of the data guided by a set of functions defined on the data. The proposed method is not dependent on any particular clustering algorithm, i.e. any clustering algorithm may be used with Mapper. We implement this method and present a few sample applications in which simple descriptions of the data present important information about its structure.
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Metadata
- Authors
- Singh, G., Memoli, F., & Carlsson, G.
- ORCID
- Carlsson, Gunnar
- Venue
- The Eurographics Association
- Publisher
- The Eurographics Association
- Published
- 2007
- OpenAlex
- W1607787316
- Cited by
- 514
- References
- 18
- Citations by year
- 2026: 9 · 2025: 33 · 2024: 33 · 2023: 37 · 2022: 39 · 2021: 54 · 2020: 73 · 2019: 74 · 2018: 44 · 2017: 29 · 2016: 26 · 2015: 16 · 2014: 18 · 2013: 5 · 2012: 6
- FWCI
- 5.06
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Eurographics
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Keywords
- Mapper algorithm, simplicial complexes, topological data analysis, high-dimensional data, 3D object recognition
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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