Gaussian Mixture Models

Gaussian Mixture Models is a probabilistic model that represents a composite distribution as a weighted sum of multiple Gaussian components, distinguishing itself from other density estimation methods by modeling data as arising from a mixture of several underlying normal distributions with varying means and covariances.

6.8K

Publications

411.8K

Citations

17.4K

Authors

3.7K

Institutions

Publications per year

2017–2026

2.4K

Authors

17.4K

  1. SC
    Sylvain Calinon

    Idiap Research Institute, Switzerland

    25 publications · 2.9K citations

    21

    h-index

  2. NB
    Nizar Bouguila

    Concordia University, Canada

    35 publications · 1.2K citations

    20

    h-index

  3. TT
    Tomoki Toda

    Nara Institute of Science and Technology, Japan

    28 publications · 2.5K citations

    19

    h-index

  4. BV
    Ba‐Ngu Vo

    Curtin University, Australia

    18 publications · 4.1K citations

    16

    h-index

  5. QM
    Q. M. Jonathan Wu

    University of Windsor, Canada

    17 publications · 984 citations

    16

    h-index

1–5 of 17.4K

Venues

  1. 51

    h-index

  2. IEEE Transactions on Signal Processing

    86 publications · 9.2K citations

    47

    h-index

  3. IEEE Transactions on Image Processing

    71 publications · 6.3K citations

    45

    h-index

  4. 42

    h-index

  5. arXiv (Cornell University)

    Repository · 158 publications · 8.8K citations

    41

    h-index

1–5