Low-rank Optimization

Low-Rank Optimization is a variational optimization method that restricts the search space to matrices of bounded or minimized rank, thereby exploiting low-rank structure to reduce complexity and enhance generalization in high-dimensional problems.

2.1K

Publications

149.9K

Citations

4.7K

Authors

1.2K

Institutions

Publications per year

2017–2026

1.1K

Authors

4.7K

  1. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    29 publications · 4.2K citations

    24

    h-index

  2. ZL
    Zhouchen Lin

    Peking University, China

    26 publications · 6.2K citations

    23

    h-index

  3. XZ
    Xi-Le Zhao

    University of Electronic Science and Technology of China, China

    22 publications · 1.6K citations

    22

    h-index

  4. YF
    Yun Fu

    Northeastern University, United States

    23 publications · 1.6K citations

    20

    h-index

  5. YM
    Yi Ma

    University of Illinois Urbana-Champaign, United States

    21 publications · 11.8K citations

    19

    h-index

1–5 of 4.7K

Venues

  1. IEEE Transactions on Image Processing

    83 publications · 8K citations

    48

    h-index

  2. arXiv (Cornell University)

    Repository · 171 publications · 9.6K citations

    45

    h-index

  3. IEEE Transactions on Signal Processing

    58 publications · 4.2K citations

    35

    h-index

  4. 34

    h-index

  5. 32

    h-index

1–5