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

Authors

229

  1. JL
    Jiwen Lu

    Tsinghua University, China

    7 publications · 1.3K citations

    7

    h-index

  2. JH
    Junlin Hu

    Nanyang Technological University, Singapore

    4 publications · 1.1K citations

    4

    h-index

  3. GD
    Guoxian Dai

    New York University, United States

    4 publications · 201 citations

    4

    h-index

  4. JX
    Jin Xie

    Tianjin University, China

    4 publications · 201 citations

    4

    h-index

  5. JZ
    Jie Zhou

    Tsinghua University, China

    3 publications · 347 citations

    3

    h-index

1–5 of 229

Venues

  1. Neurocomputing

    6 publications · 331 citations

    6

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 591 citations

    5

    h-index

  3. 3

    h-index

  4. Neural Networks

    3 publications · 162 citations

    3

    h-index

  5. IEEE Transactions on Cybernetics

    3 publications · 53 citations

    3

    h-index

1–5