Spectral Embedding

Spectral Embedding is a dimensionality reduction technique that derives low-dimensional representations of data by performing eigen-decomposition on a graph Laplacian matrix to preserve the global geometric structure of the data manifold.

260

Publications

16.2K

Citations

697

Authors

292

Institutions

Publications per year

2017–2026

129

Authors

697

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    18 publications · 1.7K citations

    18

    h-index

  2. CE
    Carey E. Priebe

    Johns Hopkins University, United States

    14 publications · 791 citations

    13

    h-index

  3. XL
    Xuelong Li

    Northwestern Polytechnical University, China

    7 publications · 610 citations

    9

    h-index

  4. MT
    Minh Tang

    Johns Hopkins University, United States

    9 publications · 644 citations

    9

    h-index

  5. RW
    Rong Wang

    Northwestern Polytechnical University, China

    6 publications · 502 citations

    7

    h-index

1–5 of 697

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 314 citations

    12

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 8 publications · 616 citations

    9

    h-index

  3. 8

    h-index

  4. 7

    h-index

  5. IEEE Transactions on Image Processing

    6 publications · 480 citations

    7

    h-index

1–5