denoiseR

R package denoiseR: Regularized Low Rank Matrix Estimation. Estimate a low rank matrix from noisy data using singular values thresholding and shrinking functions. Impute missing values with matrix completion.

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References in zbMATH (referenced in 8 articles )

Showing results 1 to 8 of 8.
Sorted by year (citations)

  1. Gerard, David; Stephens, Matthew: Unifying and generalizing methods for removing unwanted variation based on negative controls (2021)
  2. Wang, Wei; Stephens, Matthew: Empirical Bayes matrix factorization (2021)
  3. Sportisse, Aude; Boyer, Claire; Josse, Julie: Imputation and low-rank estimation with missing not at random data (2020)
  4. Griffin, Maryclare; Hoff, Peter D.: Lasso ANOVA decompositions for matrix and tensor data (2019)
  5. Husson, François; Josse, Julie; Narasimhan, Balasubramanian; Robin, Geneviève: Imputation of mixed data with multilevel singular value decomposition (2019)
  6. Archimbaud, Aurore: Unsupervised outlier detection in quality control: an overview (2018)
  7. Josse, Julie; Wager, Stefan: Bootstrap-based regularization for low-rank matrix estimation (2016)
  8. Julie Josse, Sylvain Sardy, Stefan Wager: denoiseR: A Package for Low Rank Matrix Estimation (2016) arXiv