Incremental Principal Component Analysis

Incremental Principal Component Analysis is a computational variant of principal component analysis that belongs to the class of online learning algorithms, distinguished by its ability to update the principal components incrementally as new data samples arrive, without reprocessing the entire dataset.

89

Publications

4.6K

Citations

276

Authors

154

Institutions

Publications per year

2017–2026

24

Variants

Ipca

Authors

276

  1. SO
    Seiichi Ozawa

    Kobe University, Japan

    6 publications · 227 citations

    6

    h-index

  2. SP
    Shaoning Pang

    Auckland University of Technology, New Zealand

    3 publications · 155 citations

    3

    h-index

  3. ID
    Issam Dagher

    University of Balamand, Lebanon

    3 publications · 164 citations

    3

    h-index

  4. NK
    Nikola Kasabov

    Auckland University of Technology, New Zealand

    3 publications · 155 citations

    3

    h-index

  5. UN
    Ulrich Nehmzow

    University of Essex, United Kingdom

    2 publications · 37 citations

    2

    h-index

1–5 of 276

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 161 citations

    3

    h-index

  2. 3

    h-index

  3. Pattern Recognition

    3 publications · 142 citations

    3

    h-index

  4. 2

    h-index

  5. Neural Processing Letters

    2 publications · 33 citations

    2

    h-index

1–5