Manifold Learning

Manifold Learning is a dimensionality reduction technique that models high-dimensional data as lying on a low-dimensional manifold embedded in the higher-dimensional space, distinguishing itself by leveraging the intrinsic geometric structure of the data to preserve local relationships during nonlinear projection.

2.3K

Publications

220.7K

Citations

5.8K

Authors

1.4K

Institutions

Publications per year

2017–2026

1.1K

Authors

5.8K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    34 publications · 3K citations

    25

    h-index

  2. XL
    Xuelong Li

    Xi'an Institute of Optics and Precision Mechanics, China

    25 publications · 1.8K citations

    21

    h-index

  3. DT
    Dacheng Tao

    The University of Sydney, Australia

    22 publications · 1.8K citations

    19

    h-index

  4. XH
    Xiaofei He

    Zhejiang University, China

    22 publications · 3.6K citations

    18

    h-index

  5. XL
    Xuelong Li

    Northwestern Polytechnical University, China

    19 publications · 894 citations

    18

    h-index

1–5 of 5.8K

Venues

  1. arXiv (Cornell University)

    Repository · 155 publications · 16.7K citations

    40

    h-index

  2. 37

    h-index

  3. IEEE Transactions on Image Processing

    50 publications · 6.1K citations

    34

    h-index

  4. IEEE Transactions on Cybernetics

    47 publications · 2.9K citations

    29

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 50 publications · 3K citations

    28

    h-index

1–5