High-dimensional Correlation Visualization

High-Dimensional Correlation Visualization is a visualization method designed to represent and analyze pairwise or multivariate correlation structures in datasets with a large number of dimensions, enabling the interpretation of complex dependency patterns through geometric or topological encoding.

13

Publications

512

Citations

26

Authors

16

Institutions

Publications per year

2017–2026

5

Authors

26

  1. DL
    David L. Morgan

    Monash University, Australia

    1 publications · 33 citations

    1

    h-index

  2. EC
    E. Chow

    Queen's University, Canada

    1 publications · 160 citations

    1

    h-index

  3. JJ
    J. J. Gilvarry

    RAND Corporation, United States

    1 publications · 13 citations

    1

    h-index

  4. XZ
    Xiaorui Zhai

    Utrecht University, Netherlands

    1 publications · 18 citations

    1

    h-index

  5. AT
    Alexandru Telea

    University of Groningen, Netherlands

    1 publications · 18 citations

    1

    h-index

1–5 of 26

Venues

  1. The American Statistician

    3 publications · 245 citations

    3

    h-index

  2. Fluid Phase Equilibria

    1 publications · 33 citations

    1

    h-index

  3. Journal of Geochemical Exploration

    1 publications · 11 citations

    1

    h-index

  4. Nature

    1 publications · 13 citations

    1

    h-index

  5. Econometrics and Statistics

    1 publications · 17 citations

    1

    h-index

1–5