Results 21 to 30 of about 19,816 (300)
Constrained Backtracking Matching Pursuit Algorithm for Image Reconstruction in Compressed Sensing
Applied Sciences, 2021 Image reconstruction based on sparse constraints is an important research topic in compressed sensing. Sparsity adaptive matching pursuit (SAMP) is a greedy pursuit reconstruction algorithm, which reconstructs signals without prior information of the ...
Xue Bi +5 more
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A robust matching pursuit algorithm using information theoretic learning [PDF]
, 2020 Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the ...
Gao, Y, Sun, C, Zhang, M, Blumenstein, M
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Underdetermined noisy blind separation using dual matching pursuits [PDF]
, 2004 Underdetermined blind source separation is a key application in audio where it is desirable to extract multiple sources from a stereo recording. A new variant on the stereo matching pursuit, the dual matching pursuit, is presented whereby independent ...
Sugden, P, Canagarajah, CN, Sugden, Paul
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Oblique Matching Pursuit [PDF]
IEEE Signal Processing Letters, 2007 Last version- as it will appear in IEEE SPL. IEEE Signal Processing Letters (in press)
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Online Orthogonal Matching Pursuit
CoRR, 2020 Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the sample complexity, with little or no consideration of the computation resources required.
El Mehdi Saad +2 more
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Sequential Sparse Matching Pursuit [PDF]
2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2009 We propose a new algorithm, called Sequential Sparse Matching Pursuit (SSMP), for solving sparse recovery problems. The algorithm provably recovers a k-sparse approximation to an arbitrary n-dimensional signal vector x from only O(k log(n/k)) linear measurements of x. The recovery process takes time that is only near-linear in n.
Berinde, Radu, Indyk, Piotr
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, 2008 Sparse signal approximations have become a fundamental tool in signal processing with wide ranging applications from source separation to signal acquisition.
Blumensath, T. +2 more
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Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction
IEEE Open Journal of Signal Processing, 2023 The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruction or approximation. It acts as a driving force for the development of several other greedy methods for sparse data reconstruction, and it also plays a ...
Yun-Bin Zhao, Zhi-Quan Luo
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Generalized Orthogonal Matching Pursuit [PDF]
IEEE Transactions on Signal Processing, 2012 As a greedy algorithm to recover sparse signals from compressed measurements, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we introduce an extension of the OMP for pursuing efficiency in reconstructing sparse signals.
Jian Wang 0016 +2 more
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Quantum matching pursuit algorithms [PDF]
, 2022 LAUREA MAGISTRALEIl machine learning quantistico è una disciplina emergente che combina i benefici del calcolo quantistico con tecniche di machine learning.
VANERIO, STEFANO
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