Conceptual
Browse Instruct Curate
Login

About Gaussian Mixture Models: Soft Clustering with EM

Copied

Build Gaussian mixture models from first principles: the latent-variable view of a weighted sum of Gaussians, responsibilities as Bayesian posteriors, and the EM algorithm that fits the parameters with a proof of why each iteration can only improve the likelihood. You will know how covariance constraints (full, diagonal, tied, spherical) trade flexibility for parameter count, how to seed EM with k-means and defend against local optima and covariance collapse, how BIC/AIC choose the number of components, and where GMMs earn their keep — density estimation, soft clustering, and speaker/background modeling.

Estimated Time to Complete

Only available after login

What You'll Learn

Concepts:
E-Step of the EM Algorithm Gaussian Mixture Model Selecting the Number of Mixture Components Responsibilities in Mixture Models Covariance Structures in Gaussian Mixtures Soft Versus Hard Cluster Assignment Latent Variable Formulation of Mixture Models Singular Covariance Collapse M-Step of the EM Algorithm Local Optima in EM Density Estimation with Mixture Models K-Means Seeding for EM Initialization Universal Background Model Multivariate Gaussian Distribution Monotonic Likelihood Ascent of EM Mixture Weights

What you will learn

No introduction video available

About Demerzel

D

Guide profile coming soon.