Kernel Metric Learning is a metric learning method that employs kernel functions to implicitly map input data into a higher-dimensional space where distances are optimized to enhance discrimination between instances, distinguishing it from linear or non-kernel-based metric learning approaches by leveraging the kernel trick to capture nonlinear relationships.
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2017–2026
37
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116
Geodesic flow kernel for unsupervised domain adaptation
Boqing Gong, Yuan Shi, Fei Sha et al. · 2012 · 2.2K citations
Face recognition based on image sets
Hakan Çevıkalp, Bill Triggs · 2010 · 505 citations · Full text
The Kernel Trick for Distances
Bernhard Schölkopf · MPG.PuRe (Max Planck Society) · 2000 · 498 citations · Full text
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Institutions
167
Chinese Academy of Sciences
Beijing, China
6 h-index
13 publications
1.3K citations
Jiangnan University
Wuxi, China
5 h-index
17 publications
697 citations
University of Surrey
Guildford, United Kingdom
4 h-index
5 publications
162 citations
Hong Kong Polytechnic University
Hong Kong, Hong Kong
4 h-index
8 publications
438 citations
Oklahoma State University
Stillwater, United States
4 h-index
6 publications
194 citations
Harbin Institute of Technology
Harbin, China
4 h-index
8 publications
614 citations
Ghent University
Ghent, Belgium
3 h-index
6 publications
276 citations
Microsoft (United States)
Redmond, United States
3 h-index
3 publications
596 citations
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- IEEE Transactions on Image Processing
8 publications · 1.3K citations
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- Pattern Recognition
5 publications · 389 citations
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- Neurocomputing
4 publications · 137 citations
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- arXiv (Cornell University)
Repository · 3 publications · 51 citations
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- PLoS ONE
3 publications · 390 citations
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