Regularized Matrix Factorization

Regularized Matrix Factorization is a matrix factorization method that incorporates penalty terms into the optimization objective to constrain model parameters, thereby reducing overfitting by explicitly differentiating it from standard matrix factorization techniques through the inclusion of regularization terms such as L2 or sparsity constraints.

383

Publications

32.6K

Citations

1.1K

Authors

426

Institutions

Publications per year

2017–2026

223

Authors

1.1K

  1. JL
    Jin‐Xing Liu

    Qufu Normal University, China

    7 publications · 249 citations

    8

    h-index

  2. XL
    Xuelong Li

    Xi'an Institute of Optics and Precision Mechanics, China

    7 publications · 349 citations

    7

    h-index

  3. YG
    Ying-Lian Gao

    Qufu Normal University, China

    6 publications · 225 citations

    6

    h-index

  4. YX
    Yong Xu

    Harbin Institute of Technology, China

    6 publications · 543 citations

    6

    h-index

  5. ZX
    Zenglin Xu

    University of Electronic Science and Technology of China, China

    5 publications · 202 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. Neurocomputing

    17 publications · 882 citations

    16

    h-index

  2. arXiv (Cornell University)

    Repository · 15 publications · 1K citations

    13

    h-index

  3. 10

    h-index

  4. Knowledge-Based Systems

    8 publications · 588 citations

    8

    h-index

  5. Linear Algebra and its Applications

    8 publications · 1.5K citations

    8

    h-index

1–5