An Improved EM algorithm

TL;DR

This paper analyzes the sensitivity of the EM algorithm to initial values and demonstrates that initializing with K-medoids improves performance over K-means or random initialization in Gaussian mixture models.

Contribution

It provides an empirical comparison of different initialization methods for EM, highlighting the advantage of K-medoids initialization.

Findings

K-medoids initialization outperforms K-means and random initialization.

EM's performance depends heavily on initial parameter choice.

The paper offers insights into EM's convergence behavior.

Abstract

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.