An Improved EM algorithm
This is an incremental improvement for practitioners using EM algorithms in clustering applications.
The paper investigates the sensitivity of the Expectation-Maximization (EM) algorithm to initialization methods on Gaussian mixture models, finding that EM initialized with K-medoids outperforms both random initialization and K-means initialization.
In this paper, we firstly give a brief introduction of expectation maximization (EM) algorithm, and then discuss the initial value sensitivity of expectation maximization algorithm. Subsequently, we give a short proof of EM's convergence. Then, we implement experiments with the expectation maximization algorithm (We implement all the experiments on Gaussion mixture model (GMM)). Our experiment with expectation maximization is performed in the following three cases: initialize randomly; initialize with result of K-means; initialize with result of K-medoids. The experiment result shows that expectation maximization algorithm depend on its initial state or parameters. And we found that EM initialized with K-medoids performed better than both the one initialized with K-means and the one initialized randomly.