Mixture Initialization Based on Prior Data Visual Analysis

The initialization is known to be a critical task for running a mixture estimation algorithm. A majority of approaches existing in the literature are related to initialization of the expectation-maximization algorithm widely used in this area. This study focuses on the initialization of the recursive mixture estimation for the case of normal components, where the mentioned methods are not applicable. Its key part is a choice of the initial statistics of normal components. Several initialization techniques based on visual analysis of prior data are discussed. Validation experiments are presented.