Principal Component Analysis

Principal Component Analysis is a dimensionality reduction technique in multivariate statistics that transforms a set of correlated variables into a smaller set of uncorrelated linear combinations, known as principal components, which successively capture the maximum variance in the data.

15.2K

Publications

1.3M

Citations

34.6K

Authors

6.1K

Institutions

Publications per year

2017–2026

4.6K

Authors

34.6K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    50 publications · 3.3K citations

    31

    h-index

  2. SJ
    S. Joe Qin

    University of Southern California, United States

    38 publications · 7.4K citations

    28

    h-index

  3. XY
    Xuefeng Yan

    East China University of Science and Technology, China

    33 publications · 2.2K citations

    25

    h-index

  4. SW
    Svante Wold

    Umeå University, Sweden

    29 publications · 24.7K citations

    25

    h-index

  5. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    30 publications · 8.2K citations

    23

    h-index

1–5 of 34.6K

Venues

  1. Journal of Chemometrics

    229 publications · 22.2K citations

    69

    h-index

  2. Chemometrics and Intelligent Laboratory Systems

    202 publications · 28.6K citations

    67

    h-index

  3. Pattern Recognition

    216 publications · 19.6K citations

    67

    h-index

  4. Journal of the American Statistical Association

    134 publications · 24.8K citations

    61

    h-index

  5. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 88 publications · 31.6K citations

    58

    h-index

1–5