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

Authors

1.3K

  1. FS
    Fei Sha

    University of Southern California, United States

    9 publications · 844 citations

    9

    h-index

  2. JL
    Jiwen Lu

    Tsinghua University, China

    8 publications · 956 citations

    8

    h-index

  3. BH
    Barbara Hammer

    Bielefeld University, Germany

    7 publications · 602 citations

    7

    h-index

  4. DT
    Dacheng Tao

    The University of Sydney, Australia

    7 publications · 430 citations

    7

    h-index

  5. KQ
    Kilian Q. Weinberger

    Cornell University, United States

    7 publications · 6.3K citations

    7

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 40 publications · 4.3K citations

    22

    h-index

  2. Pattern Recognition

    19 publications · 620 citations

    14

    h-index

  3. Neurocomputing

    17 publications · 868 citations

    13

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 13 publications · 1.4K citations

    13

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 473 citations

    12

    h-index

1–5