Principal Components

Principal Components is a statistical construct that represents the orthogonal directions of maximum variance in a dataset, derived through linear transformation to reduce dimensionality while preserving the greatest amount of information.

11.3K

Publications

914.9K

Citations

33K

Authors

6.8K

Institutions

Publications per year

2017–2026

2.2K

Authors

33K

  1. IT
    Ian T. Jolliffe

    University of Aberdeen, United Kingdom

    22 publications · 13.6K citations

    21

    h-index

  2. SW
    Svante Wold

    Umeå University, Sweden

    20 publications · 17.7K citations

    19

    h-index

  3. WJ
    W. J. Krzanowski

    University of Exeter, United Kingdom

    16 publications · 1.9K citations

    16

    h-index

  4. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    19 publications · 901 citations

    16

    h-index

  5. JK
    Jeremy K. Nicholson

    Imperial College London, United Kingdom

    16 publications · 1.4K citations

    15

    h-index

1–5 of 33K

Venues

  1. Journal of the American Statistical Association

    89 publications · 18.7K citations

    52

    h-index

  2. PLoS ONE

    128 publications · 6.6K citations

    46

    h-index

  3. The Annals of Statistics

    50 publications · 8.7K citations

    39

    h-index

  4. IEEE Transactions on Geoscience and Remote Sensing

    51 publications · 10.4K citations

    35

    h-index

  5. International Journal of Climatology

    65 publications · 4.2K citations

    34

    h-index

1–5