Linear Discriminant Analysis

Linear Discriminant Analysis is a supervised linear transformation technique used in dimensionality reduction that maximizes class separability by projecting features onto a lower-dimensional space while preserving discriminative information.

13.7K

Publications

923.1K

Citations

43.6K

Authors

7.6K

Institutions

Publications per year

2017–2026

2.9K

Authors

43.6K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    39 publications · 2.7K citations

    27

    h-index

  2. BS
    Berkman Sahiner

    University of Michigan, United States

    32 publications · 2.9K citations

    26

    h-index

  3. HC
    Heang‐Ping Chan

    University of Michigan, United States

    32 publications · 2.9K citations

    25

    h-index

  4. DZ
    David Zhang

    Hong Kong Polytechnic University, Hong Kong SAR China

    31 publications · 3.2K citations

    24

    h-index

  5. JY
    Jieping Ye

    Arizona State University, United States

    29 publications · 3.6K citations

    23

    h-index

1–5 of 43.6K

Venues

  1. 65

    h-index

  2. Journal of Agricultural and Food Chemistry

    150 publications · 10.3K citations

    64

    h-index

  3. Pattern Recognition

    171 publications · 13.1K citations

    55

    h-index

  4. PubMed

    Repository · 245 publications · 13.4K citations

    47

    h-index

  5. Journal of the American Statistical Association

    80 publications · 18.3K citations

    45

    h-index

1–5