Work

Noisy low-rank matrix completion with general sampling distribution

Year: 2014

Type: article

Abstract: In the present paper, we consider the problem of matrix completion with noise. Unlike previous works, we consider quite general sampling distribution and we do not need to know or to estimate the variance of the noise. Two new nuclear-norm penalized estimators are proposed, one of them of “square-root” type. We analyse their performance under high-dimensional scaling and provide non-asymptotic bounds on the Frobenius norm error. Up to a logarithmic factor, these performance guarantees are minimax optimal in a number of circumstances.

Source: Bernoulli

DOI: 10.3150/12-bej486

Author: Olga Klopp

Institutions: Université Paris Nanterre, Centre de Recherche en Économie et Statistique, Modélisation aléatoire de Paris Nanterre

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W2095534914

FWCI: 6.343

Cites: 22

Cited by: 126

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