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CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
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Cited by
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Ma, D., Gulani, V., Seiberlich, N., Liu, K., Sunshine, J. L., Duerk, J. L., & Griswold, M. A. (2013) · Nature · doi:10.1038/nature11971 · Cited by 1,577
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals
Tropp, J. A., Laska, J. N., Duarte, M. F., Romberg, J. K., & Baraniuk, R. G. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2034811 · Cited by 1,089
Self-citationNon-convex Optimization for Machine Learning
Jain, P., & Kar, P. (2017) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000058 · Cited by 364
Digital Twin: Values, Challenges and Enablers
Rasheed, A., San, O., & Kvamsdal, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01719 · Cited by 81
Emerging data technology in structural health monitoring: compressive sensing technology
Bao, Y., Li, H., & Ou, J. (2014) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-013-0064-1 · Cited by 43
Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
Caiafa, C. F., Solé-Casals, J., Marti-Puig, P., Zhe, S., & Tanaka, T. (2020) · Applied Sciences · doi:10.3390/app10238481 · Cited by 33
A closed-loop compressive-sensing-based neural recording system
Zhang, J., Mitra, S., Suo, Y., Cheng, A., Xiong, T., Michon, F., Welkenhuysen, M., Kloosterman, F., Chin, P. S., Hsiao, S., et al. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/036005 · Cited by 33
Sparsity-Aware Learning and Compressed Sensing: An Overview
Theodoridis, S., Kopsinis, Y., & Slavakis, K. (2014) · Academic Press Library in Signal Processing · doi:10.1016/b978-0-12-396502-8.00023-1 · Cited by 14
Metadata
- Authors
- Needell, D., & Tropp, J. A.
- Venue
- Applied and Computational Harmonic Analysis
- Publisher
- Elsevier BV
- Published
- 2009-05
- Volume
- 26
- Issue
- 3
- Pages
- 301-321
- ISSN
- 1063-5203 (ISSN-L)
- OpenAlex
- W2289917018
- Cited by
- 4,326
- References
- 0
- FWCI
- 181.69
- Type
- journal article
- Language
- en
- Open access
- bronze
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.