منابع مشابه
Oblique Projection Matching Pursuit
Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient. In this paper, the performance of OMP is shown to be dependent on how well information of the underlying signals is...
متن کاملAMP: Assembly Matching Pursuit
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متن کاملTheory of matching pursuit
We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound is tighter than the KPCA bound of Shawe-Taylor et al [7] and highly predictive of the size of the subspace needed to capture most of the variance in the data. We analyse a second matching pursuit algorithm called kern...
متن کاملA Posteriori Quantized Matching Pursuit
This paper studies quantization error in the context of Matching Pursuit coded streams and proposes a new coefficient quantization scheme taking benefit of the Matching Pursuit properties. The coefficients energy in Matching Pursuit indeed decreases with the iteration number, and the decay rate can be upper-bounded with an exponential curve driven by the redundancy of the dictionary. The redund...
متن کاملTuning Free Orthogonal Matching Pursuit
Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated k0 or noise variance σ , both of which are unavailable in many pract...
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ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2007
ISSN: 1070-9908
DOI: 10.1109/lsp.2007.898317