Kernel Learning by Spectral Representation and Gaussian Mixtures
Luis Ricardo Pena-Llamas, Ramón O. Guardado-Medina, Arturo Garcia, Andres Méndez-Vázquez · Applied Sciences · 2023
One of the main tasks in kernel methods is the selection of adequate mappings into higher dimension in order to improve class classification. However, this tends to be time consuming, and it may not finish with the best separation between classes. Therefore, there is a need for better methods that are able to extract distance and class separation from data. This work presents a novel approach for learning such mappings by using locally stationary kernels, spectral representations and Gaussian mixtures.