Principal Component Analysis Features is a dimensionality reduction technique that transforms a set of correlated variables into a new set of uncorrelated variables, called principal components, which capture the directions of maximum variance in the data.
723
Publications
37.4K
Citations
2.4K
Authors
950
Institutions
Publications per year
2017–2026
279
Parents
Publications
723
1–5 of 723
Authors
2.4K
- DZ
4
h-index
- RL
3
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- YS
3
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- GN
3
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- HG
3
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1–5 of 2.4K
Institutions
950
Chinese Academy of Sciences
Beijing, China
11 h-index
40 publications
3K citations
Shanghai Jiao Tong University
Shanghai, China
9 h-index
20 publications
670 citations
Harbin Institute of Technology
Harbin, China
9 h-index
16 publications
1.3K citations
Hong Kong Polytechnic University
Hong Kong, Hong Kong
8 h-index
14 publications
805 citations
Chongqing University
Chongqing, China
8 h-index
22 publications
1.4K citations
Northwestern Polytechnical University
Xi'an, China
6 h-index
15 publications
1.3K citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
7 publications
196 citations
Northeastern University
Shenyang, China
5 h-index
18 publications
516 citations
1–8 of 950
Venues
- IEEE Access
14 publications · 370 citations
14
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- IEEE Transactions on Neural Networks and Learning Systems
9 publications · 944 citations
10
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- Scientific Reports
9 publications · 258 citations
9
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- Sensors
8 publications · 535 citations
8
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- IEEE Transactions on Instrumentation and Measurement
7 publications · 868 citations
7
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1–5