Nonlinear Metric Learning
Nonlinear Metric Learning is a metric learning approach that extends linear distance metric learning by employing nonlinear transformations—typically through kernel methods or deep neural networks—to capture complex, nonlinear relationships in data while learning a task-specific distance function that enhances discriminative performance in non-Euclidean or high-dimensional spaces.
78
Publications
5.3K
Citations
229
Authors
103
Institutions
Publications per year
2017–2026
32
Parents
78
Discriminative Deep Metric Learning for Face Verification in the Wild
Junlin Hu, Jiwen Lu, Yap‐Peng Tan · 2014 · 692 citations · Full text
The Kernel Trick for Distances
Bernhard Schölkopf · MPG.PuRe (Max Planck Society) · 2000 · 498 citations · Full text
Multi-manifold deep metric learning for image set classification
Jiwen Lu, Gang Wang, Weihong Deng et al. · 2015 · 181 citations
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103
Nanyang Technological University
Singapore, Singapore
10 h-index
18 publications
2.8K citations
Tsinghua University
Beijing, China
6 h-index
10 publications
809 citations

Chinese Academy of Sciences
Beijing, China
5 h-index
13 publications
650 citations
New York University
New York, United States
4 h-index
14 publications
693 citations
Wuhan University
Wuhan, China
4 h-index
7 publications
219 citations
Nanjing University of Science and Technology
Nanjing, China
3 h-index
10 publications
302 citations
Michigan State University
East Lansing, United States
3 h-index
3 publications
152 citations

Beijing University of Technology
Beijing, China
3 h-index
3 publications
92 citations
1–8 of 103
6 publications · 331 citations
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Repository · 5 publications · 591 citations
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TPAMI · 3 publications · 304 citations
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3 publications · 162 citations
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3 publications · 53 citations
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