The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
A fast randomized algorithm for the approximation of matrices
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Citations
Cites
Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization
Gu, M., & Eisenstat, S. C. (1996) · SIAM Journal on Scientific Computing · doi:10.1137/0917055 · Cited by 677
Improved Approximation Algorithms for Large Matrices via Random Projections
Sarlos, T. (2006) · 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06) · doi:10.1109/focs.2006.37 · Cited by 646
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
Ailon, N., & Chazelle, B. (2006) · Proceedings of the thirty-eighth annual ACM symposium on Theory of Computing · doi:10.1145/1132516.1132597 · Cited by 379
On the Compression of Low Rank Matrices
Cheng, H., Gimbutas, Z., Martinsson, P. G., & Rokhlin, V. (2005) · SIAM Journal on Scientific Computing · doi:10.1137/030602678 · Cited by 301
Self-citationEstimating the Largest Eigenvalue by the Power and Lanczos Algorithms with a Random Start
Kuczyński, J., & Woźniakowski, H. (1992) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/0613066 · Cited by 251
Incomplete Cross Approximation in the Mosaic-Skeleton Method
Tyrtyshnikov, E. (2000) · Computing · doi:10.1007/s006070070031 · Cited by 249
Efficient computation of the DFT with only a subset of input or output points
Sorensen, H. V., & Burrus, C. S. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.205723 · Cited by 245
Some Applications of the Rank Revealing QR Factorization
Chan, T. F.-C., & Hansen, P. C. (1992) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0913043 · Cited by 158
Estimating Extremal Eigenvalues and Condition Numbers of Matrices
Dixon, J. D. (1983) · SIAM Journal on Numerical Analysis · doi:10.1137/0720053 · Cited by 106
Checking approximate computations over the reals
Ar, S., Blum, M., Codenotti, B., & Gemmell, P. S. (1993) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/167088.167288 · Cited by 40
Cited by
Randomized algorithms for the low-rank approximation of matrices
Liberty, E., Woolfe, F., Martinsson, P.-G., Rokhlin, V., & Tygert, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0709640104 · Cited by 559
Self-citationA Randomized Algorithm for Principal Component Analysis
Rokhlin, V., Szlam, A., & Tygert, M. (2010) · SIAM Journal on Matrix Analysis and Applications · doi:10.1137/080736417 · Cited by 422
Self-citationAn Algorithm for the Principal Component Analysis of Large Data Sets
Halko, N., Martinsson, P.-G., Shkolnisky, Y., & Tygert, M. (2011) · SIAM Journal on Scientific Computing · doi:10.1137/100804139 · Cited by 291
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Metadata
- Authors
- Woolfe, F., Liberty, E., Rokhlin, V., & Tygert, M.
- Venue
- Applied and Computational Harmonic Analysis
- Publisher
- Elsevier BV
- Published
- 2008-11
- Volume
- 25
- Issue
- 3
- Pages
- 335-366
- ISSN
- 1063-5203 (ISSN-L)
- OpenAlex
- W4213311204
- Cited by
- 280
- References
- 11
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Woolfe, F., Liberty, E., Rokhlin, V., & Tygert, M. (2008). A fast randomized algorithm for the approximation of matrices. Applied and Computational Harmonic Analysis. https://doi.org/10.1016/j.acha.2007.12.002
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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.
