Correlation Principal Component Analysis

Correlation Principal Component Analysis is a variant of principal component analysis that applies dimensionality reduction to a dataset by diagonalizing the correlation matrix rather than the covariance matrix, thereby standardizing variables to unit variance prior to transformation.

34

Publications

1.5K

Citations

86

Authors

47

Institutions

Publications per year

2017–2026

7

Authors

86

  1. PH
    Peter H. Westfall

    Texas Tech University, United States

    1 publications · 12 citations

    1

    h-index

  2. SD
    Shailesh Dewangan

    National Institute of Technology Rourkela, India

    1 publications · 13 citations

    1

    h-index

  3. CK
    Chandan Kumar Biswas

    National Institute of Technology Rourkela, India

    1 publications · 13 citations

    1

    h-index

  4. RB
    Richard B. McCammon

    United States Geological Survey, United States

    1 publications · 42 citations

    1

    h-index

  5. CL
    Curtis L. Hartling

    Oregon Health & Science University, United States

    1 publications · 33 citations

    1

    h-index

1–5 of 86

Venues

  1. Journal of Multivariate Analysis

    2 publications · 33 citations

    2

    h-index

  2. Journal of Marketing Research

    1 publications · 14 citations

    1

    h-index

  3. 1

    h-index

  4. Statistical Applications in Genetics and Molecular Biology

    SAGMB · 1 publications · 18 citations

    1

    h-index

  5. Multivariate Behavioral Research

    MBR · 1 publications · 12 citations

    1

    h-index

1–5