Spatial Feature Selection
Spatial Feature Selection is a dimensionality reduction technique that identifies and retains the most relevant spatial attributes or variables for modeling spatial patterns and processes, differentiating itself by explicitly accounting for spatial autocorrelation and spatial heterogeneity in the selection criteria.
98
Publications
7.8K
Citations
335
Authors
188
Institutions
Publications per year
2017–2026
47
98
Skeleton based action recognition with convolutional neural network
Yong Du, Yun Fu, Liang Wang · 2015 · 430 citations
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188
Philipps University of Marburg
Marburg, Germany
4 h-index
8 publications
2.1K citations
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
Cologne, Germany
3 h-index
3 publications
100 citations
University of Iowa
Iowa City, United States
3 h-index
6 publications
98 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
11 publications
1.4K citations
Wuhan University
Wuhan, China
3 h-index
8 publications
281 citations
University of Southern California
Los Angeles, United States
2 h-index
4 publications
122 citations

Xidian University
Xi'an, China
2 h-index
10 publications
1.6K citations

Ghent University
Ghent, Belgium
2 h-index
8 publications
928 citations
1–8 of 188
6 publications · 608 citations
6
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3 publications · 113 citations
3
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JN · 2 publications · 60 citations
2
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2 publications · 33 citations
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2 publications · 105 citations
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1–5