Low-rank Approximation

Low-Rank Approximation is a matrix approximation technique in numerical linear algebra that replaces a given matrix with a lower-rank matrix to minimize approximation error, typically by capturing dominant singular components while discarding less significant ones.

3.6K

Publications

284K

Citations

7.4K

Authors

1.7K

Institutions

Publications per year

2017–2026

1.6K

Authors

7.4K

  1. ZL
    Zhouchen Lin

    Peking University, China

    27 publications · 9.9K citations

    24

    h-index

  2. XZ
    Xi-Le Zhao

    University of Electronic Science and Technology of China, China

    27 publications · 1.7K citations

    24

    h-index

  3. YM
    Yi Ma

    University of Illinois Urbana-Champaign, United States

    24 publications · 15.5K citations

    22

    h-index

  4. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    24 publications · 6.2K citations

    21

    h-index

  5. TH
    Ting‐Zhu Huang

    University of Electronic Science and Technology of China, China

    22 publications · 1.5K citations

    20

    h-index

1–5 of 7.4K

Venues

  1. arXiv (Cornell University)

    Repository · 284 publications · 13.6K citations

    52

    h-index

  2. SIAM Journal on Matrix Analysis and Applications

    127 publications · 11.1K citations

    49

    h-index

  3. 47

    h-index

  4. IEEE Transactions on Signal Processing

    99 publications · 7.4K citations

    46

    h-index

  5. IEEE Transactions on Image Processing

    76 publications · 7.9K citations

    43

    h-index

1–5