Convergence of the EM Algorithm for Gaussian Mixtures with Unbalanced Mixing Coefficients

Understand

The speed of convergence of the Expectation Maximization (EM) algorithm for Gaussian mixture model fitting is known to be dependent on the amount of overlap among the mixture components.

• In this paper, we study the impact of mixing coefficients on the convergence of EM.
• We show that when the mixture components exhibit some overlap, the convergence of EM becomes slower as the dynamic range among the mixing coefficients increases.
• We propose a deterministic anti-annealing algorithm, that significantly improves the speed of convergence of EM for such mixtures with unbalanced mixing coefficients.