Matching pursuit (MP) algorithm finds a sub-optimal solution to the problem of an adaptive approximation of a signal in a redundant set (dictionary) of functions. Commonly used with dictionaries of Gabor functions, it offers several advantages in time-frequency analysis of signals, in particular EEG/MEG.
Apr 3, 2014
We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme.
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Matching pursuit should represent the signal by just a few atoms, such as the three at the centers of the clearly visible ellipses.
We analyse matching pursuit for kernel principal components analysis by proving that the sparse subspace it produces is a sample compression scheme. We show ...
Matching pursuit algorithms introduced by Mallat and Zhang [366] are greedy algorithms that optimize approximations by selecting dictionary vectors one by one.
Aug 24, 2022 · Abstract:Greedy approaches in general, and orthogonal matching pursuit in particular, are the most commonly used sparse recovery techniques ...
May 28, 2024 · At its core, Orthogonal Matching Pursuit (OMP) is a fascinating bridge between theoretical concepts and practical applications.
This work analyses matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression ...
[PDF] Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
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Abstract—We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy ...
The orthogonal matching pursuit (OMP) algorithm [7] is a methodology to solve NP-hard sparsity problems. ... The support L is defined as the indices of non-zero ...
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