The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Message passing algorithms for compressed sensing: I. motivation and construction
Open accessAbstract
In a recent paper, the authors proposed a new class of low-complexity iterative thresholding algorithms for reconstructing sparse signals from a small set of linear measurements. The new algorithms are broadly referred to as AMP, for approximate message passing. This is the first of two conference papers describing the derivation of these algorithms, connection with the related literature, extensions of the original framework, and new empirical evidence. In particular, the present paper outlines the derivation of AMP from standard sum-product belief propagation, and its extension in several directions. We also discuss relations with formal calculations based on statistical mechanics methods.
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Cites
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,845
Self-citationAtomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,085
Self-citationMessage-passing algorithms for compressed sensing
Donoho, D. L., Maleki, A., & Montanari, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0909892106 · Cited by 2,416
Self-citationRichardson, T., & Urbanke, R. (2008) · Cambridge University Press eBooks · doi:10.1017/cbo9780511791338 · Cited by 2,271
Information, Physics, and Computation
Mézard, M., & Montanari, A. (2009) · doi:10.1093/acprof:oso/9780198570837.001.0001 · Cited by 1,913
Self-citationConstructing Free-Energy Approximations and Generalized Belief Propagation Algorithms
Yedidia, J. S., Freeman, W. T., & Weiss, Y. (2005) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.850085 · Cited by 1,617
Solution of 'Solvable model of a spin glass'
Thouless, D. J., Anderson, P. W., & Palmer, R. G. (1977) · Philosophical Magazine · doi:10.1080/14786437708235992 · Cited by 1,286
Bayesian Compressive Sensing Via Belief Propagation
Baron, D., Sarvotham, S., & Baraniuk, R. G. (2010) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2009.2027773 · Cited by 498
Spin Glasses: A Challenge for Mathematicians
Talagrand, M. (2003) · Cited by 324
A CDMA multiuser detection algorithm on the basis of belief propagation
Kabashima, Y. (2003) · Journal of Physics A: Mathematical and General · doi:10.1088/0305-4470/36/43/030 · Cited by 324
Cited by
Liu, L., Larsson, E. G., Yu, W., Popovski, P., Stefanovic, C., & de Carvalho, E. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2018.2844952 · Cited by 510
Expectation-Maximization Gaussian-Mixture Approximate Message Passing
Vila, J. P., & Schniter, P. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2272287 · Cited by 501
AMP-Inspired Deep Networks for Sparse Linear Inverse Problems
Borgerding, M., Schniter, P., & Rangan, S. (2017) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2017.2708040 · Cited by 455
Ke, M., Gao, Z., Wu, Y., Gao, X., & Schober, R. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2967175 · Cited by 417
Non-convex Optimization for Machine Learning
Jain, P., & Kar, P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000058 · Cited by 364
Metadata
- Authors
- Donoho, D. L., Maleki, A., & Montanari, A.
- Venue
- IEEE Information Theory Workshop 2010 (ITW 2010)
- Publisher
- IEEE
- Published
- 2010-01
- Pages
- 1-5
- OpenAlex
- W2963206527
- Cited by
- 542
- References
- 18
- FWCI
- 45.54
- Type
- conference paper
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
