High-dimensional simplexes for supermetric search
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 267898361
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-68474-1_7
- Title of conference / published proceedings
- Similarity Search and Applications - 10th International Conference, SISAP 2017, Proceedings
- First page
- 96
- Volume
- 10609 LNCS
- Issue
- -
- ISSN
- 0302-9743
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- 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
-
2
- Research group(s)
-
B - Systems
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article defines the n-simplex projection, which uses the geometry of Hilbert spaces to develop estimates and bounds for distances in Euclidean and non-Euclidean spaces. Among the citations to this article, eight of them strongly rely on the n-simplex projection, to the extent that the new work could not have occurred without the results given in this paper. They include research from Masaryk University, Brno and ISTI-CNR Pisa, world-leading research groups in the domain of content-based retrieval. Proofs of correctness for the methods given are included in an extended (cited) version published in ArXiV.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
