Singular Value Decomposition

Singular Value Decomposition is a matrix factorization method that decomposes a real or complex matrix into the product of three matrices—where the first and third are unitary (or orthogonal in the real case) and the second is a diagonal matrix with non-negative real numbers on the diagonal—thereby revealing the canonical structure of linear transformations in terms of singular values and associated singular vectors.

6.7K

Publications

453.1K

Citations

15.9K

Authors

3.5K

Institutions

Publications per year

2017–2026

1.8K

Authors

15.9K

  1. BD
    Bart De Moor

    KU Leuven, Belgium

    24 publications · 5.7K citations

    18

    h-index

  2. FS
    Francesco Soldovieri

    Istituto per il Rilevamento Elettromagnetico dell'Ambiente, Italy

    22 publications · 1.3K citations

    18

    h-index

  3. GH
    Gene H. Golub

    Stanford University, United States

    19 publications · 8.5K citations

    18

    h-index

  4. RP
    Rocco Pierri

    University of Campania "Luigi Vanvitelli", Italy

    25 publications · 961 citations

    18

    h-index

  5. FT
    Franklin T. Luk

    Cornell University, United States

    22 publications · 1.4K citations

    16

    h-index

1–5 of 15.9K

Venues

  1. SIAM Journal on Matrix Analysis and Applications

    117 publications · 12.8K citations

    47

    h-index

  2. IEEE Transactions on Signal Processing

    99 publications · 7.1K citations

    46

    h-index

  3. Linear Algebra and its Applications

    80 publications · 3.7K citations

    35

    h-index

  4. IEEE Transactions on Geoscience and Remote Sensing

    67 publications · 10.7K citations

    35

    h-index

  5. arXiv (Cornell University)

    Repository · 117 publications · 6.2K citations

    33

    h-index

1–5