Electronic Journal of Statistics

Rank penalized estimators for high-dimensional matrices

Olga Klopp

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In this paper we consider the trace regression model. Assume that we observe a small set of entries or linear combinations of entries of an unknown matrix A0 corrupted by noise. We propose a new rank penalized estimator of A0. For this estimator we establish general oracle inequality for the prediction error both in probability and in expectation. We also prove upper bounds for the rank of our estimator. Then, we apply our general results to the problems of matrix completion and matrix regression. In these cases our estimator has a particularly simple form: it is obtained by hard thresholding of the singular values of a matrix constructed from the observations.

Article information

Electron. J. Statist., Volume 5 (2011), 1161-1183.

First available in Project Euclid: 6 October 2011

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Zentralblatt MATH identifier

Primary: 62J99: None of the above, but in this section 62H12: Estimation 60B20: Random matrices (probabilistic aspects; for algebraic aspects see 15B52) 60G15: Gaussian processes

Matrix completion low rank matrix estimation recovery of the rank statistical learning


Klopp, Olga. Rank penalized estimators for high-dimensional matrices. Electron. J. Statist. 5 (2011), 1161--1183. doi:10.1214/11-EJS637. https://projecteuclid.org/euclid.ejs/1317906992

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