Learning Gaussian mixture models by structural risk minimization

Gaussian mixture models are often used for probability density estimation in pattern recognition and machine learning systems. Selecting an optimal number of components in mixture model is important to ensure an accurate and efficient estimate. In this paper, a methodology based on structural risk minimization is presented which trades off between training error and the model complexity. The main contribution of this work is that we give the capacity of an N-component GMM. When applied to unsupervised learning and speech recognition system, the new method shows good performance compared to classical model selection methods.