Metric Learning For Classification
Metric Learning for Classification is a supervised machine learning framework that learns an optimal distance metric from labeled data to enhance the separation of distinct classes while preserving the proximity of similar instances in the transformed feature space.
462
Publications
44.9K
Citations
1.3K
Authors
463
Institutions
Publications per year
2017–2026
191
Parents
462
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
Marco Cuturi · 2013 · 2.2K citations
Bo Pang, Lillian Lee · 2005 · 2.2K citations · Full text
Information-theoretic metric learning
Jason V. Davis, Brian Kulis, Prateek Jain et al. · 2007 · 2K citations
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Chinese Academy of Sciences
Beijing, China
19 h-index
66 publications
3.7K citations
University of Technology Sydney
Sydney, Australia
12 h-index
24 publications
1.1K citations
Tsinghua University
Beijing, China
12 h-index
28 publications
1.9K citations
Nanyang Technological University
Singapore, Singapore
11 h-index
18 publications
1.4K citations
University of Chinese Academy of Sciences
Beijing, China
11 h-index
17 publications
1.2K citations

Xi'an Jiaotong University
Xi'an, China
10 h-index
23 publications
1K citations
Nanjing University
Nanjing, China
10 h-index
24 publications
826 citations
Nanjing University of Science and Technology
Nanjing, China
9 h-index
24 publications
526 citations
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Repository · 40 publications · 4.3K citations
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19 publications · 620 citations
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17 publications · 868 citations
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TPAMI · 13 publications · 1.4K citations
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Conference · 14 publications · 473 citations
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