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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.
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“FactoMineR: An \proglang
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“Monte-Carlo SURE: a black-box optimization of regularization parameters for general denoising algorithms.”
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“Selecting the Number of Components in PCA Using Cross-Validation Approximations.”
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“mice: Multivariate imputation by chained equations in R.”
van Buuren S, Groothuis-Oudshoorn K (2011) · 2011
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“Handling missing values in exploratory multivariate data analysis methods.”
Josse J, Husson F (2012) · 2012
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“Smooth blockwise iterative thresholding: a smooth fixed point estimator based on the likelihood’s block gradient.”
Sardy S (2012) · 2012
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“MissForest - Nonparametric missing value imputation for mixed-type data.”
Stekhoven D, Bühlmann P (2012) · 2012
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Flexible Imputation of Missing Data
van Buuren S (2012) · 2012
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“Unbiased risk estimates for singular value thresholding and spectral estimators.”
Candes EJ, Sing-Long CA, Trzasko JD (2013) · 2013
FactoMineR: Multivariate Exploratory Data Analysis and Data Mining
Husson F, Josse J, Le S, Mazet J (2015) · 2015
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“Adaptive shrinkage of singular values.”
Josse J, Sardy S (2015) · 2015
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Data and text mining Visualization, inference, classification
Lebart L (2015) · 2015
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Multiple Factor Analysis with R
Pagès J (2015) · 2015
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“Regularized PCA to denoise and visualize data.”
Verbanck M, Husson F, Josse J (2015) · 2015
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“A principal components method to impute missing values for mixed data.”
Audigier V, Husson F, Josse J (2016) · 2016
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Cited alongside, same era.
“Reconstruction of a low-rank matrix in the presence of Gaussian noise.”
Shabalin AA, Nobel AB (2013) · 2013
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“Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection.”
Deledalle CA, Vaiter S, Fadili JM, Peyré G (2014) · 2014
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“Recursive partitioning for missing data imputation in the presence of interaction effects.”
Doove LL, Van Buuren S, Dusseldorp E (2014) · 2014
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“OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage.”
Nadakuditi R (2014) · 2014
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“Comparison of random forest and parametric imputation models for imputing missing data using MICE: A CALIBER study.”
Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H (2014) · 2014
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covmat: Covariance Matrix Estimation
Arora R (2015) · 2015
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“Bootstrap-Based Regularization for Low-Rank Matrix Estimation.”
Josse J, Wager S (2016) · 2016
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nlshrink: Non-Linear Shrinkage Estimation of Population Eigenvalues and Covariance Matrices
Ramprasad P (2016) · 2016
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“Data Augmentation via Levy Processes.”
Wager S, Fithian W, Liang P (2016) · 2016
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“Optimal Shrinkage of Singular Values.”
Gavish M, Donoho DL (2017) · 2017
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“Quantile universal threshold for model selection.”
Giacobino C, Sardy S, Diaz Rodriguez J, Hengartner N (2017) · 2017
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Exploratory Multivariate Analysis by Example Using R
Husson F, Le S, Pagès J (2017) · 2017
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corpcor: Efficient Estimation of Covariance and (Partial) Correlation
Schafer J, Opgen-Rhein R, Zuber V, Ahdesmaki M, Silva APD, Strimmer K (2017) · 2017
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“Nice latent variable models have log-rank.”
Udell M, Townsend A (2017) · 2017
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