In many practical contexts, Gaussian Mixtures are used as density approximators due to their versatility and representation capabilities. In some scenarios, it might be convenient to approximate a set of Gaussian densities with a single one, according to criteria which aim to preserve information while reducing the model complexity. This task can be seen as a particular case of the Gaussian Mixture Reduction problem, where the goal is to find a mixture of reduced size yielding the least dissimilarity from the original mixture. From a different perspective, this can be interpreted as a data fusion process, where several Gaussian densities are fused into one. In this work, an information-theoretic class of measures will be explored in the analytical and numerical properties in order to provide insights on their nature when adopted in a Gaussian mixture reduction or data fusion process.
|Titolo:||Likeness-Based dissimilarity measures for Gaussian Mixture Reduction and Data Fusion|
D'ORTENZIO, ALESSANDRO (Corresponding)
|Data di pubblicazione:||2021|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|
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