FITTING A GAUSSIAN MIXTURE MODEL THROUGH THE GINI INDEX

A linear combination of Gaussian components is known as a Gaussian mixture model. It is widely used in data mining and pattern recognition. In this paper, we propose a method to estimate the parameters of the density function given by a Gaussian mixture model. Our proposal is based on the Gini index, a methodology to measure the inequality degree between two probability distributions, and consists in minimizing the Gini index between an empirical distribution for the data and a Gaussian mixture model. We will show several simulated examples and real data examples, observing some of the properties of the proposed method.

Automated summary

A method for estimating the parameters of a Gaussian mixture model's density function by minimizing the Gini index between an empirical data distribution and the model was proposed in this paper. The method was validated through simulated and real data examples, demonstrating its effectiveness and properties.