Sammon Mapping

Sammon Mapping is a nonlinear dimensionality reduction technique that preserves the structure of interpoint distances in high-dimensional data by minimizing a stress function based on pairwise dissimilarities between points in the original and lower-dimensional spaces.

60

Publications

38.3K

Citations

185

Authors

95

Institutions

Publications per year

2017–2026

8

Authors

185

  1. MA
    Mayer Aladjem

    Ben-Gurion University of the Negev, Israel

    2 publications · 71 citations

    2

    h-index

  2. KV
    Konstantin V. Balakin

    Kazan Federal University, Russia

    2 publications · 132 citations

    2

    h-index

  3. NS
    Nikolay Savchuk

    Office of Diversity and Inclusion, United States

    2 publications · 132 citations

    2

    h-index

  4. YA
    Yan A. Ivanenkov

    Moscow Institute of Physics and Technology, Russia

    2 publications · 132 citations

    2

    h-index

  5. HG
    Hugo Guterman

    Ben-Gurion University of the Negev, Israel

    2 publications · 71 citations

    2

    h-index

1–5 of 185

Venues

  1. IEEE Transactions on Neural Networks

    3 publications · 301 citations

    3

    h-index

  2. 2

    h-index

  3. Pattern Recognition

    2 publications · 80 citations

    2

    h-index

  4. Journal of Computational Chemistry

    2 publications · 26 citations

    2

    h-index

  5. IEEE Transactions on Computers

    2 publications · 37 citations

    2

    h-index

1–5