Covariance Regularization

Covariance Regularization is a statistical method that belongs to the class of covariance estimation techniques, distinguished by its incorporation of constraints or penalties to ensure stable and well-conditioned estimates of spatial covariance structures in high-dimensional or undersampled settings.

75

Publications

5.2K

Citations

217

Authors

145

Institutions

Publications per year

2017–2026

23

Authors

217

  1. JP
    Jianxin Pan

    University of Manchester, United Kingdom

    2 publications · 62 citations

    2

    h-index

  2. JP
    Jean Ponce

    Institut national de recherche en sciences et technologies du numérique, France

    2 publications · 373 citations

    2

    h-index

  3. AB
    Adrien Bardes

    Courant Institute of Mathematical Sciences, United States

    2 publications · 373 citations

    2

    h-index

  4. MZ
    Min-Ling Zhang

    Southeast University, China

    2 publications · 27 citations

    2

    h-index

  5. PB
    Paolo Banelli

    University of Perugia, Italy

    2 publications · 30 citations

    2

    h-index

1–5 of 217

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 511 citations

    4

    h-index

  2. NeuroImage

    4 publications · 489 citations

    4

    h-index

  3. Signal Processing

    2 publications · 41 citations

    2

    h-index

  4. IEEE Communications Letters

    2 publications · 30 citations

    2

    h-index

  5. Journal of Neural Engineering

    2 publications · 55 citations

    2

    h-index

1–5