Dimensionality Reduction
Dimensionality Reduction is a subfield of Data Analytics that reduces the number of random variables under consideration by identifying a smaller set of latent or transformed features while preserving essential structure or information in the data.
41.6K
Publications
3.5M
Citations
72.1K
Authors
8.2K
Institutions
Publications per year
2017–2026
15.8K
41.6K
Laurens van der Maaten, Geoffrey E. Hinton · Journal of Machine Learning Research · 2008 · 35.7K citations
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Sam T. Roweis, Lawrence K. Saul · Science · 2000 · 15K citations
1–5 of 41.6K
72.1K
77
h-index
69
h-index
Xi'an Institute of Optics and Precision Mechanics, China
106 publications · 14.9K citations
59
h-index
56
h-index
53
h-index
1–5 of 72.1K
8.2K
Stanford University
Stanford, United States
125 h-index
877 publications
259.1K citations

Chinese Academy of Sciences
Beijing, China
119 h-index
2K publications
167.8K citations
University of California, Berkeley
Berkeley, United States
107 h-index
696 publications
101.3K citations
Carnegie Mellon University
Pittsburgh, United States
105 h-index
938 publications
101.5K citations
Nanyang Technological University
Singapore, Singapore
100 h-index
829 publications
64K citations

Harbin Institute of Technology
Harbin, China
98 h-index
1.1K publications
93.8K citations
University of Illinois Urbana-Champaign
Urbana, United States
98 h-index
669 publications
143.5K citations
Tsinghua University
Beijing, China
96 h-index
1.4K publications
94.4K citations
1–8 of 8.2K
TPAMI · 494 publications · 153.9K citations
167
h-index
Repository · 1.7K publications · 103.6K citations
129
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562 publications · 72.1K citations
127
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979 publications · 74.2K citations
123
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511 publications · 57.3K citations
122
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1–5