Improving Laplacian Pyramids Regression with Localization in Frequency and Time
Entity
UAM. Departamento de Ingeniería InformáticaDate
2022-10Citation
ESANN 2022 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) 5-7 October 2022ISBN
978-287-587-084-1Funded by
This research was supported by the Israel Science Foundation [Grant 1144/20]Subjects
InformáticaAbstract
Auto-Adaptive Laplacian Pyramids (ALP) is an iterative
kernel-based regression model. It constructs a multi-scale representation
of the train data, where the multi-scale modes are average residuals. In
this work, we propose two extensions of the model. The first is a hybrid
approach that combines ALP with Empirical Mode Decomposition to provide
localization in the frequency domain. The second modifies ALP to fit
datasets with non-uniform noise, which is achieved by computing the optimal
stopping criterion in a point-dependent manner. Experimental results
demonstrate these models for solar energy prediction and for forecasting
epidemiology infections.
Files in this item
Google Scholar:Hen, Ben
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Fernández Pascual, Ángela
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Rabin, Neta
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