Nonlinear Principal Component Analysis is a dimensionality reduction technique that extends traditional Principal Component Analysis by modeling nonlinear relationships among variables through the use of nonlinear basis functions or kernel methods.
261
Publications
26.7K
Citations
604
Authors
318
Institutions
Publications per year
2017–2026
54
Parents
Publications
261
1–5 of 261
Authors
604
- WW
10
h-index
- GL
8
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- GI
6
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- JK
6
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5
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1–5 of 604
Institutions
318
University of British Columbia
Vancouver, Canada
14 h-index
22 publications
928 citations
Tsinghua University
Beijing, China
8 h-index
18 publications
370 citations
GIPSA-Lab
Grenoble, France
7 h-index
14 publications
564 citations
Queen's University Belfast
Belfast, United Kingdom
6 h-index
17 publications
1K citations
Institut polytechnique de Grenoble
Grenoble, France
6 h-index
11 publications
453 citations
Leiden University
Leiden, The Netherlands
5 h-index
10 publications
2.8K citations
University of Maryland, College Park
College Park, United States
5 h-index
10 publications
1.7K citations
Hong Kong University of Science and Technology
Hong Kong, Hong Kong
4 h-index
10 publications
368 citations
1–8 of 318
Venues
- Journal of Climate
5 publications · 352 citations
5
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- Computers & Chemical Engineering
5 publications · 848 citations
5
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- Industrial & Engineering Chemistry Research
4 publications · 327 citations
4
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- Chemometrics and Intelligent Laboratory Systems
4 publications · 378 citations
4
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- Neural Computation
4 publications · 8.9K citations
4
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1–5