A Multicomponent proximal algorithm for Empirical Mode DecompositionReport as inadecuate




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1 Phys-ENS - Laboratoire de Physique de l-ENS Lyon

Abstract : The Empirical Mode Decomposition EMD is known to be a powerful tool adapted to the decomposition of a signal into a collection of intrinsic mode functions IMF. A key procedure in the extraction of the IMFs is the sifting process whose main drawback is to depend on the choice of an interpolation method and to have no clear convergence guarantees. We propose a convex optimization procedure in order to replace the sifting process in the EMD. The considered method is based on proximal tools, which allow us to deal with a large class of constraints such as quasi-orthogonality or extrema-based constraints.

Keywords : EMD Trend-fluctuation Convex optimization Proximal algorithms





Author: Nelly Pustelnik - Pierre Borgnat - Patrick Flandrin -

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



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