Principal Component Analysis Visualization

Principal Component Analysis Visualization is a dimensionality reduction visualization technique that transforms high-dimensional data into a lower-dimensional space by projecting it onto orthogonal axes of maximum variance, enabling interpretable visual patterns through principal component scores.

59

Publications

11.6K

Citations

175

Authors

87

Institutions

Publications per year

2017–2026

18

Authors

175

  1. NF
    Nina F. Thornhill

    Imperial College London, United Kingdom

    2 publications · 56 citations

    2

    h-index

  2. PC
    Pak Chung Wong

    Pacific Northwest National Laboratory, United States

    2 publications · 48 citations

    2

    h-index

  3. KR
    K. R. Gabriel

    University of Rochester, United States

    2 publications · 3.2K citations

    2

    h-index

  4. LB
    Lynne Billard

    University of Georgia, United States

    2 publications · 135 citations

    2

    h-index

  5. HS
    Han‐Wei Shen

    The Ohio State University, United States

    1 publications · 14 citations

    1

    h-index

1–5 of 175

Venues

  1. Journal of Computational and Graphical Statistics

    JCGS · 4 publications · 444 citations

    4

    h-index

  2. PLoS ONE

    3 publications · 153 citations

    3

    h-index

  3. Analytical Chemistry

    2 publications · 310 citations

    2

    h-index

  4. Biometrika

    2 publications · 3.2K citations

    2

    h-index

  5. Bioinformatics

    2 publications · 87 citations

    2

    h-index

1–5