Bayesian Canonical Correlation Analysis

Bayesian Canonical Correlation Analysis is a statistical modeling framework within the class of Bayesian latent variable models that extends classical canonical correlation analysis by incorporating Bayesian inference to estimate latent shared structures between multivariate datasets while quantifying uncertainty through posterior distributions.

16

Publications

805

Citations

53

Authors

24

Institutions

Publications per year

2017–2026

3

Authors

53

  1. AK
    Arto Klami

    University of Helsinki, Finland

    6 publications · 347 citations

    6

    h-index

  2. SK
    Samuel Kaski

    Aalto University, Finland

    5 publications · 331 citations

    5

    h-index

  3. SV
    Seppo Virtanen

    Aalto University, Finland

    3 publications · 238 citations

    3

    h-index

  4. RH
    Riitta Hari

    Aalto University, Finland

    2 publications · 47 citations

    2

    h-index

  5. EG
    Enrico Glerean

    Aalto University, Finland

    1 publications · 11 citations

    1

    h-index

1–5 of 53

Venues

  1. PLoS ONE

    1 publications · 11 citations

    1

    h-index

  2. Botany

    1 publications · 11 citations

    1

    h-index

  3. Neural Computation

    1 publications · 71 citations

    1

    h-index

  4. Human Brain Mapping

    1 publications · 36 citations

    1

    h-index

  5. International Conference on Machine Learning

    Conference · 1 publications · 58 citations

    1

    h-index

1–5