Dimensionality Reduction For Segmentation

Dimensionality Reduction for Segmentation is a methodological framework in machine learning that reduces the number of input features while preserving structurally relevant patterns to enhance the accuracy and efficiency of image or data segmentation tasks.

34

Publications

1.8K

Citations

103

Authors

56

Institutions

Publications per year

2017–2026

14

Authors

103

  1. AG
    Alvina Goh

    National University of Singapore, Singapore

    2 publications · 322 citations

    2

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  2. YY
    Yilong Yin

    Shandong University, China

    2 publications · 48 citations

    2

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  3. QW
    Qin Wei

    Wuhan University of Technology, China

    2 publications · 48 citations

    2

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  4. YL
    Ying Li

    University of Connecticut, United States

    2 publications · 48 citations

    2

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  5. LL
    Lili Liu

    Northwestern Polytechnical University, China

    2 publications · 48 citations

    2

    h-index

1–5 of 103

Venues

  1. 2

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  2. Oxford University Research Archive (ORA) (University of Oxford)

    Repository · 1 publications · 40 citations

    1

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  3. University of Twente Research Information

    Repository · 1 publications · 18 citations

    1

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  4. Information Sciences

    1 publications · 13 citations

    1

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  5. Journal of the American Society for Mass Spectrometry

    JASMS · 1 publications · 24 citations

    1

    h-index

1–5