Covariance Types in Gaussian Mixture

In Gaussian Mixture Models, there are four covariance types available:

1. Full: Each component has its own general covariance matrix, allows each component to have a unique shape, orientation, and size in all dimensions.
2. Tied: All components share the same general covariance matrix, forces all components to share the same shape and orientation, promoting a more spherical distribution.
3. Diag: Each component has its own diagonal covariance matrix, permits components to have different variances along each dimension but assumes no correlation between dimensions.
4. Spherical: Each component has its own single variance, assumes that the shape of each component is spherical, with a single variance for all dimensions

These covariance types in Gaussian Mixture offer flexibility in modeling the distribution of data.

Gaussian Mixture Models (GMMs) are a type of probabilistic model used for clustering and density estimation. They assist in representing data as a combination of different Gaussian distributions, where each distribution is characterized by its mean, covariance, and weight.

The Gaussian mixture model probability distribution class allows us to estimate the parameters of such a mixture distribution.

In this article, we’ll delve into four types of covariances with GMM models.