Dimensionality Reduction For Scatterplot Matrices

Dimensionality Reduction for Scatterplot Matrices is a methodological framework that enables the systematic simplification of high-dimensional datasets into lower-dimensional representations while preserving pairwise variable relationships, specifically to facilitate the effective visualization and interpretation of patterns within scatterplot matrices.

14

Publications

1.3K

Citations

40

Authors

26

Institutions

Publications per year

2017–2026

5

Authors

40

  1. MS
    Michael Sedlmair

    University of Stuttgart, Germany

    3 publications · 363 citations

    3

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  2. TM
    Tamara Munzner

    University of British Columbia, Canada

    2 publications · 342 citations

    2

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  3. MT
    Melanie Tory

    University of Victoria, Canada

    2 publications · 342 citations

    2

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  4. CH
    Catherine Hurley

    National University of Ireland, Maynooth, Ireland

    1 publications · 84 citations

    1

    h-index

  5. DS
    Damith Senanayake

    The University of Melbourne, Australia

    1 publications · 12 citations

    1

    h-index

1–5 of 40

Venues

  1. Biomedical Signal Processing and Control

    1 publications · 19 citations

    1

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  2. 1

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  3. Control Engineering Practice

    1 publications · 61 citations

    1

    h-index

  4. Biosystems

    1 publications · 12 citations

    1

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  5. Journal of Computational and Graphical Statistics

    JCGS · 1 publications · 84 citations

    1

    h-index

1–5