Bayesian Principal Component Analysis

Bayesian Principal Component Analysis is a probabilistic dimensionality reduction method that extends classical principal component analysis by incorporating a Bayesian framework to estimate principal components through posterior inference over model parameters, thereby enabling uncertainty quantification and automatic rank determination.

84

Publications

5.1K

Citations

298

Authors

167

Institutions

Publications per year

2017–2026

23

Authors

298

  1. BH
    Biao Huang

    University of Alberta, Canada

    4 publications · 283 citations

    4

    h-index

  2. MZ
    Miaomiao Zhang

    University of Utah, United States

    2 publications · 53 citations

    2

    h-index

  3. XJ
    Xiaomo Jiang

    Vanderbilt University, United States

    2 publications · 65 citations

    2

    h-index

  4. GL
    Guozheng Li

    Tongji University, China

    2 publications · 36 citations

    2

    h-index

  5. PT
    P. Thomas Fletcher

    University of Utah, United States

    2 publications · 53 citations

    2

    h-index

1–5 of 298

Venues

  1. Computational Statistics & Data Analysis

    CSDA · 2 publications · 58 citations

    2

    h-index

  2. BMC Bioinformatics

    2 publications · 224 citations

    2

    h-index

  3. The Astrophysical Journal

    ApJ · 2 publications · 81 citations

    2

    h-index

  4. Journal of Process Control

    1 publications · 41 citations

    1

    h-index

  5. 1

    h-index

1–5