Using the Gini Index for a Gaussian Mixture Model
| Autor: | Martha Lorena Avendaño-Garrido, Adriana Laura López-Lobato |
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| Rok vydání: | 2020 |
| Předmět: |
Iterative method
Gaussian 05 social sciences 020206 networking & telecommunications Probability density function 02 engineering and technology Density estimation Mixture model Empirical distribution function symbols.namesake ComputingMethodologies_PATTERNRECOGNITION 0502 economics and business 0202 electrical engineering electronic engineering information engineering symbols Applied mathematics Probability distribution 050207 economics Parametric statistics Mathematics |
| Zdroj: | Advances in Computational Intelligence ISBN: 9783030608866 MICAI (2) |
| DOI: | 10.1007/978-3-030-60887-3_35 |
| Popis: | A Gaussian mixture model is a weighted sum of parametric Gaussian components. These parametric density functions are widely used in data mining and pattern recognition. In this work we propose an efficient method to model a density function as a Gaussian mixture through an iterative algorithm that allow us to estimate the parameters of the model for a given data set. For this purpose we use the Gini Index, a measure of the inequality degree between two probability distributions. The Gini Index is obtained by finding the solution of an optimization problem. Our model consists in minimizing the Gini Index between an empirical distribution and a parametric distribution that is a Gaussian mixture. We will show some simulated examples and real data examples, with two widely used datasets, to observe the efficiency and properties of our model. |
| Databáze: | OpenAIRE |
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