The Annals of Statistics

Matrix estimation by Universal Singular Value Thresholding

Sourav Chatterjee

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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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Ann. Statist., Volume 43, Number 1 (2015), 177-214.

First available in Project Euclid: 9 December 2014

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Zentralblatt MATH identifier

Primary: 62F12: Asymptotic properties of estimators 62G05: Estimation
Secondary: 05C99: None of the above, but in this section 60B20: Random matrices (probabilistic aspects; for algebraic aspects see 15B52)

Matrix completion matrix estimation sochastic blockmodel latent space model distance matrix covariance matrix singular value decomposition low rank matrices graphons


Chatterjee, Sourav. Matrix estimation by Universal Singular Value Thresholding. Ann. Statist. 43 (2015), no. 1, 177--214. doi:10.1214/14-AOS1272.

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