Unsupervised Metric Learning

Unsupervised Metric Learning is a metric learning paradigm that belongs to the broader class of distance metric learning methods, distinguished by its ability to learn a distance function from unlabeled data without relying on explicit class labels or pairwise constraints derived from labels.

118

Publications

6.6K

Citations

358

Authors

168

Institutions

Publications per year

2017–2026

60

Authors

358

  1. SK
    Samuel Kaski

    Aalto University, Finland

    4 publications · 131 citations

    4

    h-index

  2. YC
    Yiu‐ming Cheung

    Hong Kong Baptist University, Hong Kong SAR China

    3 publications · 149 citations

    3

    h-index

  3. WZ
    Wei‐Shi Zheng

    Sun Yat-sen University, China

    2 publications · 379 citations

    2

    h-index

  4. HY
    Hong-Xing Yu

    Sun Yat-sen University, China

    2 publications · 379 citations

    2

    h-index

  5. PS
    Paramveer S. Dhillon

    University of Pennsylvania, United States

    2 publications · 26 citations

    2

    h-index

1–5 of 358

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 212 citations

    9

    h-index

  2. Neurocomputing

    4 publications · 271 citations

    4

    h-index

  3. Neural Networks

    3 publications · 182 citations

    3

    h-index

  4. Pattern Recognition Letters

    3 publications · 186 citations

    3

    h-index

  5. Expert Systems with Applications

    3 publications · 54 citations

    3

    h-index

1–5