Determination of the number of components based on class separability in mixture-based classifiers

We propose a novel method for determining the number of components in mixture-based classifiers. Each class-conditional probabilistic density function can be approximated well by the mixture of Gaussian components. However, the performance of this classifier depends on the number of components. In our proposed method, determination of the number of components is based on both probabilistic likelihood and class separability. The results of experiments confirmed the effectiveness and the property