Analysis of HighDimensional Signal Data by Manifold Learning and ConvolutionsReportar como inadecuado




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1 Department of Mathematics - University of Hamburg

Abstract : A novel concept for the analysis of high-dimensional signal data is proposed. To this end, customized techniques from manifold learning are combined with convolution transforms, being based on wavelets. The utility of the resulting method is supported by numerical examples concerning low-dimensional parameterizations of scale modulated signals and solutions to the wave equation at varying initial conditions.





Autor: Mijail Guillemard - Armin Iske -

Fuente: https://hal.archives-ouvertes.fr/



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