Work

Noisy Low-rank Matrix Completion with General Sampling Distribution

Year: 2012

Type: preprint

Abstract: In the present paper we consider the problem of matrix completion with noise for general sampling schemes. Unlike previous works, in our construction we do not need to know or to evaluate the sampling distribution or the variance of the noise. We propose new nuclear-norm penalized estimators, one of them of the ''square-root'' type. We prove that, up to a logarithmic factor, our estimators achieve optimal rates with respect to the estimation error.

Source: RePEc: Research Papers in Economics

Author: Klopp, Olga

Institution: Centre de Recherche en Économie et Statistique

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3098807808

Cites: 24

Cited by: 150

Related to: 10