Sparse Solution of Underdetermined Systems of Linear Equations by Stagewise Orthogonal Matching Pursuit

By David L. Donoho, Yaakov Tsaig, Iddo Drori, Jean-Luc Starck. IEEE Transactions on Information Theory, 2012

1,427 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c6207645e4b1cc91e8fad537e2c0637dab102f21

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Paolo Rocca
2. Giacomo Oliveri
3. Andrea Massa
4. Angshul Majumdar
5. Ying-Chang Liang
6. Feng Jiang
7. Vidya Muthukumar
8. Simon Robert Arridge

In Computational Theory and Mathematics

1. Ping Wang
2. Paolo Rocca
3. Ying-Chang Liang
4. Andrea Massa
5. Simon Robert Arridge
6. Carola-Bibiane Schönlieb
7. Dusist Niyato
8. Anant Sahai

In Computer Vision and Pattern Recognition

1. Yakoub Bazi
2. Carola-Bibiane Schönlieb
3. Angshul Majumdar
4. Paolo Rocca
5. Ying-Chang Liang
6. Feng Jiang
7. Andrea Massa
8. Simon Robert Arridge

Citations in the corpus

• Understanding overfitting peaks in generalization error: Analytical risk curves for l2 and l1 penalized interpolation (2019, background): “Many approaches can be brought to bear on this problem, including the replica and cavity approaches from statistical physics[13, 14, 9, 15] and the message-passing/belief propagation approaches[7, 5, 20].”
• Land-Use Classification With Compressive Sensing Multifeature Fusion (2015, methodology): “In the following, we briefly introduce an algorithm called the stagewise orthogonal matching pursuit (StOMP) [15], which will be exploited in this letter.”
• Solving inverse problems using data-driven models (2019, background): “Luo, J.-S. Pang and D. Ralph (1996), Mathematical programs with equilibrium constraints, Cambridge University Press.”
• Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, methodology): “The traditional detection algorithms such as iterative algorithm [191] can be used.”
• Image Compressed Sensing Using Convolutional Neural Network (2020, methodology): “These algorithms include matching pursuit [18], orthogonal matching pursuit [19], and stage-wise orthogonal matching pursuit method [34].”
• RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction (2017, background): “To quote Donoho [47] ‘noiseless under-determined problems behave like noisy well determined problems’.”
• Compressive Sensing in Electromagnetics - A Review (2015, background): “, orthogonal matching pursuit (OMP) [29], stagewise OMP (StOMP) [30], and compressive sampling matching pursuit (CoSaMP) [31]) aimed at finding the sparsest x through an iterative search have been proposed.”
• Harmless interpolation of noisy data in regression (2019, background): “There is rich literature on sparsity-seeking algorithms that are motivated by iterative perspectives from signal processing (orthogonal matching pursuit (OMP) [12] and stagewise OMP [13]) and convex relaxation (basis pursuit/Lasso [26]).”