Auto-adaptive multi-scale Laplacian Pyramids for modeling non-uniform data
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
ElsevierDate
2020-08-01Citation
10.1016/j.engappai.2020.103682
Engineering Applications of Artificial Intelligence 93 (2020): 103682
ISSN
0952-1976 (print)DOI
10.1016/j.engappai.2020.103682Funded by
They wish to thank Prof. Ronald R. Coifman for helpful remarks. They 525 also gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at Universidad Autónoma de Madrid. Funding: This work was supported by Spanish grants of the Ministerio de Ciencia, Innovación y Universidades [grant numbers: TIN2013-42351-P, TIN2015-70308-REDT, TIN2016-76406-P]; project CASI-CAM-CM supported by Madri+d 530 [grant number: S2013/ICE-2845]; project FACIL supported by Fundación BBVA (2016); and the UAM–ADIC Chair for Data Science and Machine LearningProject
Gobierno de España. TIN2013-42351-P; Gobierno de España. TIN2015-70308-REDT; Gobierno de España. TIN2016-76406-P; Comunidad de Madrid. S2013/ICE-2845/CASI-CAMEditor's Version
https://doi.org/10.1016/j.engappai.2020.103682Subjects
Adaptive stopping; Kernel methods; Laplacian Pyramids; Multi-scale interpolation; Non-uniform data; Overfitting; InformáticaRights
© ElsevierEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
Kernel-based techniques have become a common way for describing the local and global relationships of data samples that are generated in real-world processes. In this research, we focus on a multi-scale kernel based technique named Auto-adaptive Laplacian Pyramids (ALP). This method can be useful for function approximation and interpolation. ALP is an extension of the standard Laplacian Pyramids model that incorporates a modified Leave-One-Out Cross Validation procedure, which makes the method stable and automatic in terms of parameters selection without extra cost. This paper introduces a new algorithm that extends ALP to fit datasets that are non-uniformly distributed. In particular, the optimal stopping criterion will be point-dependent with respect to the local noise level and the sample rate. Experimental results over real datasets highlight the advantages of the proposed multi-scale technique for modeling and learning complex, high dimensional data
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Google Scholar:Fernández Pascual, Ángela
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Rabin, Neta
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Fishelov, Dalia
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Dorronsoro Ibero, José Ramón
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