Semi-supervised Feature Learning

Semi-Supervised Feature Learning is a machine learning paradigm that leverages both labeled and unlabeled data to learn feature representations by exploiting the underlying data structure, distinguishing itself from purely supervised or unsupervised approaches through the joint use of label information and data distribution assumptions.

424

Publications

23.1K

Citations

1.6K

Authors

554

Institutions

Publications per year

2017–2026

280

Authors

1.6K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    8 publications · 723 citations

    8

    h-index

  2. YY
    Yi Yang

    University of Technology Sydney, Australia

    7 publications · 1.1K citations

    7

    h-index

  3. TL
    Tianrui Li

    Southwest Jiaotong University, China

    5 publications · 172 citations

    5

    h-index

  4. XJ
    Xiao‐Yuan Jing

    Wuhan University, China

    5 publications · 136 citations

    5

    h-index

  5. ZM
    Zhigang Ma

    Carnegie Mellon University, United States

    5 publications · 540 citations

    5

    h-index

1–5 of 1.6K

Venues

  1. arXiv (Cornell University)

    Repository · 18 publications · 632 citations

    15

    h-index

  2. Neurocomputing

    12 publications · 694 citations

    12

    h-index

  3. IEEE Access

    12 publications · 490 citations

    12

    h-index

  4. Knowledge-Based Systems

    11 publications · 350 citations

    11

    h-index

  5. 9

    h-index

1–5