Bayesian Nonnegative Matrix Factorization

Bayesian Nonnegative Matrix Factorization is a probabilistic matrix decomposition method that incorporates Bayesian inference to estimate nonnegative latent factors and their uncertainties by placing prior distributions over the factor matrices, thereby enabling principled uncertainty quantification and automatic model selection within the framework of nonnegative matrix factorization.

22

Publications

685

Citations

99

Authors

56

Institutions

Publications per year

2017–2026

8

Variants

Bayesian Nmf

Authors

99

  1. KY
    Kazuyoshi Yoshii

    Kyoto University, Japan

    2 publications · 30 citations

    2

    h-index

  2. MS
    Miriam S. Udler

    Massachusetts General Hospital, United States

    2 publications · 51 citations

    2

    h-index

  3. JC
    José C. Florez

    Massachusetts General Hospital, United States

    2 publications · 51 citations

    2

    h-index

  4. JC
    Jen‐Tzung Chien

    National Yang Ming Chiao Tung University, Taiwan

    2 publications · 47 citations

    2

    h-index

  5. CC
    Chung Chien Hsu

    1 publications · 12 citations

    1

    h-index

1–5 of 99

Venues

  1. 3

    h-index

  2. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 2 publications · 23 citations

    2

    h-index

  3. Molecular BioSystems

    1 publications · 31 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 36 citations

    1

    h-index

  5. Diabetes

    1 publications · 20 citations

    1

    h-index

1–5