Matrix Normalization

Matrix Normalization is a computational method in the context of substitution matrices that standardizes matrix entries by scaling their values relative to a reference distribution or statistical framework, thereby enabling meaningful comparison across different evolutionary or scoring models.

202

Publications

10.8K

Citations

461

Authors

284

Institutions

Publications per year

2017–2026

51

Authors

461

  1. GC
    Guan‐Yuan Chen

    National Taiwan University, Taiwan

    3 publications · 50 citations

    3

    h-index

  2. QW
    Qilong Wang

    Dalian University of Technology, China

    3 publications · 440 citations

    3

    h-index

  3. FZ
    Fuzhen Zhang

    Nova Southeastern University, United States

    3 publications · 66 citations

    3

    h-index

  4. EK
    Edmundas Kazimieras Zavadskas

    Vilnius Gediminas Technical University, Lithuania

    3 publications · 235 citations

    3

    h-index

  5. HL
    Hsiao‐Wei Liao

    National Taiwan University, Taiwan

    3 publications · 50 citations

    3

    h-index

1–5 of 461

Venues

  1. Linear Algebra and its Applications

    14 publications · 602 citations

    12

    h-index

  2. Journal of Multivariate Analysis

    6 publications · 102 citations

    6

    h-index

  3. arXiv (Cornell University)

    Repository · 4 publications · 109 citations

    4

    h-index

  4. Signal Processing

    4 publications · 137 citations

    4

    h-index

  5. Applied Soft Computing

    3 publications · 78 citations

    3

    h-index

1–5