Combining Gaussian Mixture Models

A Gaussian mixture model (GMM) estimates a probability density function using the expectation-maximization algorithm. However, it may lead to a poor performance or inconsistency. This paper analytically shows that performance of a GMM can be improved in terms of Kullback-Leibler divergence with a committee of GMMs with different initial parameters. Simulations on synthetic datasets demonstrate that a committee of as few as 10 models outperforms a single model.

• Type: Conference
• Year: 2004
• Where: IDEAL
• Authors: Hyoungjoo Lee, Sungzoon Cho
• Keywords: Expectation-maximization Algorithm, Gaussian Mixture Model, IDEAL 2004, Probability Density Function