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On a Connection between Kernel PCA and Metric Multidimensional Scaling
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Metadata
- Authors
- Williams, C. K. I.
- Affiliations
- University of Edinburgh (GB)
- Corresponding author
- Williams, Christopher K.I.
- Venue
- Machine Learning
- Published
- 2002-01
- Volume
- 46
- Issue
- 1-3
- Pages
- 11-19
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1587720067
- Cited by
- 206
- References
- 14
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 15 · 2020: 6 · 2019: 9 · 2018: 6 · 2017: 6 · 2016: 11 · 2015: 16 · 2014: 15 · 2013: 13 · 2012: 12
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- kernel principal component analysis, metric multidimensional scaling, isotropic kernels, eigenvalue problem, stress function, dimensionality reduction, kernel methods
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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