Unsupervised Learning

Unsupervised Learning is a paradigm within machine learning and statistical learning in which an algorithm infers the underlying structure of data by modeling observed variables solely from unlabeled examples, thereby discovering latent patterns, groupings, or representations without reference to predefined target outputs.

13.7K

Publications

1.2M

Citations

30.7K

Authors

4.8K

Institutions

Publications per year

2017–2026

8K

Authors

30.7K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    120 publications · 11.6K citations

    53

    h-index

  2. XL
    Xinwang Liu

    National University of Defense Technology, China

    59 publications · 5.4K citations

    41

    h-index

  3. YY
    Yi Yang

    University of Technology Sydney, Australia

    43 publications · 9.8K citations

    35

    h-index

  4. XL
    Xuelong Li

    Northwestern Polytechnical University, China

    66 publications · 3.2K citations

    34

    h-index

  5. LJ
    Licheng Jiao

    Xidian University, China

    54 publications · 2.7K citations

    30

    h-index

1–5 of 30.7K

Venues

  1. arXiv (Cornell University)

    Repository · 1.1K publications · 169.3K citations

    129

    h-index

  2. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 199 publications · 36.8K citations

    86

    h-index

  3. 80

    h-index

  4. IEEE Transactions on Image Processing

    185 publications · 16.3K citations

    71

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 239 publications · 19.3K citations

    70

    h-index

1–5