Data Approximation

Data Approximation is a methodological framework within geometric approximation that specifically enables the simplification or representation of complex, high-dimensional datasets through lower-dimensional geometric structures while preserving essential data properties.

405

Publications

30.9K

Citations

877

Authors

415

Institutions

Publications per year

2017–2026

72

Authors

877

  1. WZ
    William Zhu

    Minnan Normal University, China

    8 publications · 768 citations

    8

    h-index

  2. FW
    Fei–Yue Wang

    Chinese Academy of Sciences, China

    6 publications · 713 citations

    6

    h-index

  3. PI
    Piotr Indyk

    Massachusetts Institute of Technology, United States

    4 publications · 107 citations

    4

    h-index

  4. BH
    Bernd Hamann

    University of California, Davis, United States

    3 publications · 83 citations

    3

    h-index

  5. DF
    Dan Feldman

    University of Haifa, Israel

    3 publications · 135 citations

    3

    h-index

1–5 of 877

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 413 citations

    11

    h-index

  2. Fundamenta Informaticae

    7 publications · 290 citations

    7

    h-index

  3. SIAM Journal on Numerical Analysis

    6 publications · 258 citations

    6

    h-index

  4. Information Sciences

    5 publications · 254 citations

    5

    h-index

  5. Journal of Approximation Theory

    4 publications · 136 citations

    4

    h-index

1–5