Adaptive Seeding for Gaussian Mixture Models

Understand

We present new initialization methods for the expectation-maximization algorithm for multivariate Gaussian mixture models.

• Our methods are adaptions of the well-known $K$-means++ initialization and the Gonzalez algorithm.
• Thereby we aim to close the gap between simple random, e.g.
• uniform, and complex methods, that crucially depend on the right choice of hyperparameters.