N. Pustelnik (ENS Lyon): A multicomponent proximal algorithm for Empirical Mode Decomposition

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Date(s) - 18/10/2012
14 h 00 min - 15 h 00 min

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A multicomponent proximal algorithm for Empirical Mode Decomposition\n\nBy Nelly Pustelnik\, ENS Lyon\nThe Empirical Mode Decomposition (EMD) is known to be a powerful\ntool adapted to the decomposition of a signal into a collection of\nintrinsic mode functions (IMF). A key procedure in the extraction\nof the IMFs is the sifting process whose main drawback is to depend\non the choice of an interpolation method and to have no clear\nconvergence guarantees. We propose a convex optimization procedure\nin order to replace the sifting process in the EMD. The considered\nmethod is based on proximal tools\, which allow us to deal with\na large class of constraints such as quasi-orthogonality or extrema based\nconstraints.\n

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