The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
One sketch for all: fast algorithms for compressed sensing
Abstract
Compressed Sensing is a new paradigm for acquiring the compressible signals that arise in many applications. These signals can be approximated using an amount of information much smaller than the nominal dimension of the signal. Traditional approaches acquire the entire signal and process it to extract the information. The new approach acquires a small number of nonadaptive linear measurements of the signal and uses sophisticated algorithms to determine its information content. Emerging technologies can compute these general linear measurements of a signal at unit cost per measurement.
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Cites
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Donoho, D. L. (2004) · Cited by 16,703
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,653
Self-citationStable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
Candes, E. J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.885507 · Cited by 6,839
Compressed sensing and best 𝑘-term approximation
Cohen, A., Dahmen, W., & DeVore, R. (2008) · Journal of the American Mathematical Society · doi:10.1090/s0894-0347-08-00610-3 · Cited by 1,002
A new compressive imaging camera architecture using optical-domain compression:
Takhar, D., Laska, J. N., Wakin, M. B., Duarte, M. F., Baron, D., Sarvotham, S., Kelly, K. F., & Baraniuk, R. G. (2006) · SPIE Proceedings · doi:10.1117/12.659602 · Cited by 497
Data compression and harmonic analysis
Donoho, D. L., Vetterli, M., DeVore, R. A., & Daubechies, I. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.720544 · Cited by 432
Analog-to-Information Conversion via Random Demodulation
Kirolos, S., Laska, J., Wakin, M., Duarte, M., Baron, D., Ragheb, T., Massoud, Y., & Baraniuk, R. (2006) · 2006 IEEE Dallas/CAS Workshop on Design, Applications, Integration and Software · doi:10.1109/dcas.2006.321036 · Cited by 393
On the Fast Fourier Transform of Functions with Singularities
Beylkin, G. (1995) · Applied and Computational Harmonic Analysis · doi:10.1006/acha.1995.1026 · Cited by 334
Cited by
Subspace Pursuit for Compressive Sensing Signal Reconstruction
Dai, W., & Milenkovic, O. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2016006 · Cited by 2,388
Needell, D., & Vershynin, R. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2042412 · Cited by 850
Self-citationA Review of Sparse Recovery Algorithms
Crespo Marques, E., Maciel, N., Naviner, L., Cai, H., & Yang, J. (2019) · IEEE Access · doi:10.1109/access.2018.2886471 · Cited by 268
Metadata
- Authors
- Gilbert, A. C., Strauss, M. J., Tropp, J. A., & Vershynin, R.
- Venue
- Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2007-06-11
- Pages
- 237-246
- OpenAlex
- W1974466705
- Cited by
- 270
- References
- 14
- FWCI
- 51.41
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Gilbert, A. C., Strauss, M. J., Tropp, J. A., & Vershynin, R. (2007). One sketch for all: fast algorithms for compressed sensing. Proceedings of the thirty-ninth annual ACM symposium on Theory of computing. https://doi.org/10.1145/1250790.1250824
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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.
