denoiseR: A Package for Low Rank Matrix Estimation

2016/02/03 by Julie Josse, Josse, Julie, Sylvain Sardy +3 · 2 citations
Computer Science · #Blind Source Separation Techniques #Image and Signal Denoising Methods #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.1602.01206

Abstract

We introduce denoiseR, an R package that provides a unified implementation of several state-of-the-art proposals for regularized low rank matrix estimation, along with automatic selection of the regularization parameters. We also extend these methods to allow for missing values. The regularization schemes discussed in this paper are built around singular-value shrinkage and bootstrap-based stability arguments. We illustrate how to use out package by applying it to several real and simulated datasets, and highlight strengths and weaknesses of the different implemented methods.

Cited by

• On the consistency of supervised learning with missing values
• Imputation and low-rank estimation with Missing Not At Random data

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