The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Matrix estimation by Universal Singular Value Thresholding
Open accessAbstract
Consider the problem of estimating the entries of a large matrix, when the observed entries are noisy versions of a small random fraction of the original entries. This problem has received widespread attention in recent times, especially after the pioneering works of Emmanuel Candès and collaborators. This paper introduces a simple estimation procedure, called Universal Singular Value Thresholding (USVT), that works for any matrix that has “a little bit of structure.” Surprisingly, this simple estimator achieves the minimax error rate up to a constant factor. The method is applied to solve problems related to low rank matrix estimation, blockmodels, distance matrix completion, latent space models, positive definite matrix completion, graphon estimation and generalized Bradley–Terry models for pairwise comparison.
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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
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Hapeman, D. E. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,373
A Singular Value Thresholding Algorithm for Matrix Completion
Cai, J.-F., Candès, E. J., & Shen, Z. (2010) · SIAM Journal on Optimization · doi:10.1137/080738970 · Cited by 5,809
Exact Matrix Completion via Convex Optimization
Candès, E. J., & Recht, B. (2009) · Foundations of Computational Mathematics · doi:10.1007/s10208-009-9045-5 · Cited by 4,877
Adapting to Unknown Smoothness via Wavelet Shrinkage
Donoho, D. L., & Johnstone, I. M. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476626 · Cited by 4,630
Bhatia, R. (1996) · Cited by 2,847
Stochastic blockmodels: First steps
Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983) · Social Networks · doi:10.1016/0378-8733(83)90021-7 · Cited by 2,755
Fleiss, J. L., & Agresti, A. (1991) · Journal of the American Statistical Association · doi:10.2307/2290537 · Cited by 2,641
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Metadata
- Authors
- Chatterjee, S.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2015-02-01
- Volume
- 43
- Issue
- 1
- Pages
- 177-214
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W1996023858
- Cited by
- 465
- References
- 92
- FWCI
- 36.66
- Type
- journal article
- Language
- en
- Open access
- bronze
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