Locally Linear Embedding

Locally Linear Embedding is a nonlinear dimensionality reduction method that preserves local linear relationships among neighboring data points by reconstructing each point as a weighted combination of its neighbors and mapping it to a lower-dimensional space while maintaining these reconstruction weights.

362

Publications

78.3K

Citations

1K

Authors

390

Institutions

Publications per year

2017–2026

96

Authors

1K

  1. MP
    Matti Pietikäinen

    University of Oulu, Finland

    5 publications · 302 citations

    5

    h-index

  2. LK
    Lawrence K. Saul

    University of California San Diego, United States

    5 publications · 16K citations

    5

    h-index

  3. XL
    Xuelong Li

    Xi'an Institute of Optics and Precision Mechanics, China

    5 publications · 885 citations

    5

    h-index

  4. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    4 publications · 102 citations

    4

    h-index

  5. DC
    Deng Cai

    Zhejiang University, China

    4 publications · 307 citations

    4

    h-index

1–5 of 1K

Venues

  1. Pattern Recognition

    12 publications · 1K citations

    11

    h-index

  2. IEEE Transactions on Image Processing

    10 publications · 518 citations

    10

    h-index

  3. arXiv (Cornell University)

    Repository · 9 publications · 266 citations

    9

    h-index

  4. Neurocomputing

    8 publications · 250 citations

    8

    h-index

  5. IEEE Access

    7 publications · 187 citations

    7

    h-index

1–5