Analysis of HighDimensional Signal Data by Manifold Learning and Convolutions

Mijail Guillemard (1) , Armin Iske (1)

1 Department of Mathematics - University of Hamburg

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Théorie de l'information et codage [math.IT]; Théorie de l'information [cs.IT]; Traitement du signal et de l'image [eess.SP]

Mijail Guillemard, Armin Iske. Analysis of HighDimensional Signal Data by Manifold Learning and Convolutions. SAMPTA'09, May 2009, Marseille, France. pp.General session. ⟨hal-00453449⟩