Sparse Density Estimation

Sparse Density Estimation is a density estimation method that incorporates sparsity-inducing constraints or penalties to promote parsimonious models by selecting a minimal subset of components or features necessary to accurately represent the underlying probability distribution.

86

Publications

3.3K

Citations

220

Authors

131

Institutions

Publications per year

2017–2026

20

Authors

220

  1. SC
    Sheng Chen

    University of Southampton, United Kingdom

    8 publications · 200 citations

    8

    h-index

  2. XH
    Xia Hong

    University of Reading, United Kingdom

    7 publications · 189 citations

    7

    h-index

  3. CH
    C.J. Harris

    University of Southampton, United Kingdom

    6 publications · 170 citations

    6

    h-index

  4. GP
    Gianluigi Pillonetto

    University of Padua, Italy

    2 publications · 74 citations

    2

    h-index

  5. JV
    James V. Burke

    University of Washington, United States

    2 publications · 74 citations

    2

    h-index

1–5 of 220

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 174 citations

    6

    h-index

  2. Neurocomputing

    4 publications · 87 citations

    4

    h-index

  3. Journal of Multivariate Analysis

    3 publications · 98 citations

    3

    h-index

  4. 3

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 3 publications · 78 citations

    3

    h-index

1–5