Dimensionality Reduction

Dimensionality Reduction is a subfield of Data Analytics that reduces the number of random variables under consideration by identifying a smaller set of latent or transformed features while preserving essential structure or information in the data.

41.6K

Publications

3.5M

Citations

72.1K

Authors

8.2K

Institutions

Publications per year

2017–2026

15.8K

Authors

72.1K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    262 publications · 21.9K citations

    77

    h-index

  2. DT
    Dacheng Tao

    The University of Sydney, Australia

    141 publications · 18K citations

    69

    h-index

  3. XL
    Xuelong Li

    Xi'an Institute of Optics and Precision Mechanics, China

    106 publications · 14.9K citations

    59

    h-index

  4. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    114 publications · 24.9K citations

    56

    h-index

  5. YX
    Yong Xu

    Harbin Institute of Technology, China

    81 publications · 9.2K citations

    53

    h-index

1–5 of 72.1K

Venues

  1. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 494 publications · 153.9K citations

    167

    h-index

  2. arXiv (Cornell University)

    Repository · 1.7K publications · 103.6K citations

    129

    h-index

  3. IEEE Transactions on Geoscience and Remote Sensing

    562 publications · 72.1K citations

    127

    h-index

  4. Pattern Recognition

    979 publications · 74.2K citations

    123

    h-index

  5. IEEE Transactions on Image Processing

    511 publications · 57.3K citations

    122

    h-index

1–5