Random Projection Trees

Random Projection Trees is a hierarchical space-partitioning data structure that recursively splits high-dimensional data using random projections to enable efficient approximate nearest neighbor search.

17

Publications

1.6K

Citations

37

Authors

23

Institutions

Publications per year

2017–2026

3

Variants

Rp-trees

Authors

37

  1. SD
    Sanjoy Dasgupta

    University of California San Diego, United States

    4 publications · 681 citations

    4

    h-index

  2. DY
    Donghui Yan

    Institute of Forest Ecology, Environment and Protection, China

    3 publications · 536 citations

    3

    h-index

  3. YF
    Yoav Freund

    AT&T (United States), United States

    3 publications · 619 citations

    3

    h-index

  4. GH
    Gang Hua

    Microsoft (United States), United States

    2 publications · 101 citations

    2

    h-index

  5. ZL
    Zhenpeng Li

    Dali University, China

    2 publications · 50 citations

    2

    h-index

1–5 of 37

Venues

  1. 2009 IEEE Conference on Computer Vision and Pattern Recognition

    Conference · 2 publications · 101 citations

    2

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 23 citations

    1

    h-index

  3. 1

    h-index

  4. arXiv (Cornell University)

    Repository · 1 publications · 62 citations

    1

    h-index

  5. IEEE Transactions on Big Data

    1 publications · 36 citations

    1

    h-index

1–5