Gaussian Mixture Model & Expectation Maximization
Gaussian Mixture Model & Expectation Maximization #
A Gaussian Mixture Model represents data as a weighted combination of multiple Gaussian distributions.
It is commonly used for soft clustering and density estimation.
Key takeaway:
K-means gives hard cluster membership.GMM gives probabilities of belonging to each cluster.
- Gaussian Mixture Model
- soft clustering
- mixing coefficients
- latent variables
- likelihood and log-likelihood
- Expectation-Maximization algorithm
- E-step and M-step
- responsibilities
- convergence
Motivation ☆ #
Many real datasets are not described well by one Gaussian distribution.