Supervised Metric Learning

Supervised Metric Learning is a subfield of metric learning that, as a genus, belongs to the broader category of distance metric learning methods, and, by differentia, specifically leverages labeled training data to learn a distance function that enhances discriminative performance by minimizing distances between instances of the same class and maximizing distances between instances of different classes.

118

Publications

6.4K

Citations

380

Authors

178

Institutions

Publications per year

2017–2026

55

Authors

380

  1. JS
    Jimeng Sun

    Georgia Institute of Technology, United States

    5 publications · 524 citations

    5

    h-index

  2. AH
    Amaury Habrard

    Laboratoire Hubert Curien, France

    4 publications · 180 citations

    4

    h-index

  3. BN
    Bac Nguyen

    Ghent University, Belgium

    3 publications · 175 citations

    3

    h-index

  4. BD
    Bernard De Baets

    Ghent University, Belgium

    3 publications · 175 citations

    3

    h-index

  5. JH
    Jianying Hu

    IBM (United States), United States

    2 publications · 194 citations

    2

    h-index

1–5 of 380

Venues

  1. Pattern Recognition

    10 publications · 449 citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 10 publications · 252 citations

    10

    h-index

  3. Neurocomputing

    5 publications · 343 citations

    5

    h-index

  4. IEEE Transactions on Image Processing

    3 publications · 194 citations

    3

    h-index

  5. Pattern Recognition Letters

    3 publications · 179 citations

    3

    h-index

1–5