Subspace Learning

Subspace Learning is a dimensionality reduction technique that identifies a lower-dimensional linear or nonlinear subspace within the original high-dimensional data space by preserving intrinsic structural relationships among data points.

3K

Publications

219.6K

Citations

6.8K

Authors

1.6K

Institutions

Publications per year

2017–2026

1.2K

Variants

Subspace Analysis

Authors

6.8K

  1. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    45 publications · 13.5K citations

    32

    h-index

  2. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    49 publications · 2.9K citations

    28

    h-index

  3. RV
    René Vidal

    Johns Hopkins University, United States

    35 publications · 6.6K citations

    26

    h-index

  4. DT
    Dacheng Tao

    The University of Sydney, Australia

    29 publications · 3.2K citations

    25

    h-index

  5. ZL
    Zhouchen Lin

    Peking University, China

    25 publications · 9.1K citations

    23

    h-index

1–5 of 6.8K

Venues

  1. IEEE Transactions on Image Processing

    92 publications · 8.1K citations

    49

    h-index

  2. 47

    h-index

  3. Pattern Recognition

    103 publications · 6.4K citations

    42

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 66 publications · 17.6K citations

    40

    h-index

  5. IEEE Transactions on Signal Processing

    89 publications · 6.1K citations

    38

    h-index

1–5