The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Near-Optimal Column-Based Matrix Reconstruction
Abstract
We consider low-rank reconstruction of a matrix using a subset of its columns and present asymptotically optimal algorithms for both spectral norm and Frobenius norm reconstruction. The main tools we introduce to obtain our results are (i) the use of fast approximate SVD-like decompositions for column-based matrix reconstruction, and (ii) two deterministic algorithms for selecting rows from matrices with orthonormal columns, building upon the sparse representation theorem for decompositions of the identity that appeared in [J. D. Batson, D. A. Spielman, and N. Srivastava, Twice-Ramanujan sparsifiers, in Proceedings of the 41st Annual ACM Symposium on Theory of Computing (STOC), 2009, pp. 255--262].
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Cites
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
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Computational Advertising: Techniques for Targeting Relevant Ads
Woodruff, D. P. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000060 · Cited by 866
Metadata
- Authors
- Boutsidis, C., Drineas, P., & Magdon-Ismail, M.
- Venue
- SIAM Journal on Computing
- Published
- 2014-01
- Volume
- 43
- Issue
- 2
- Pages
- 687-717
- ISSN
- 0097-5397 (ISSN-L)
- 1095-7111
- OpenAlex
- W2547648546
- Cited by
- 169
- References
- 1
- FWCI
- 19.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- Boutsidis, C., Drineas, P., & Magdon-Ismail, M. (2014). Near-Optimal Column-Based Matrix Reconstruction. SIAM Journal on Computing. https://doi.org/10.1137/12086755x
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