2014
DOI: 10.1016/j.sigpro.2014.03.014
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Empirical mode decomposition revisited by multicomponent non-smooth convex optimization

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Cited by 56 publications
(41 citation statements)
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“…The construction of the modes makes our method complete because their sum retrieves the original signal, in contrast to [11] where the reconstruction is not guaranteed. Computational cost is similar to EMD's.…”
Section: Discussionmentioning
confidence: 99%
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“…The construction of the modes makes our method complete because their sum retrieves the original signal, in contrast to [11] where the reconstruction is not guaranteed. Computational cost is similar to EMD's.…”
Section: Discussionmentioning
confidence: 99%
“…A multicomponent non-smooth convex optimization approach to EMD was introduced by Pustelnik et al in [10], and it was more deeply explained in [11]. The optimization problem to be solved is the following:…”
Section: Optimization-based Approaches To Emdmentioning
confidence: 99%
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“…Bi-dimensional empirical mode decomposition (BEMD) was developed as a two-dimensional version of EMD. Through BEMD two-dimensional signals or data can be decomposed into a series of bi-intrinsic mode functions (BIMFs) (Bhuiyan et al, 2008a,b;Huang et al, 2010;Chen et al, 2014;Pustelnik et al, 2014).…”
Section: Introductionmentioning
confidence: 99%
“…This ability turns out to be of primary importance in terms of computational complexity in the context of some large-scale problems (see e.g. [1,[6][7][8][9][10][11]). Block-coordinate versions of various proximal algorithms have been recently proposed, such as the forward-backward algorithm [12][13][14][15][16][17][18][19], the Alternating Direction Method of Mutipliers [20,21], and the Douglas-Rachford algorithm [12], where some of the block indices in {1, .…”
Section: Introductionmentioning
confidence: 99%