softImpute: Matrix Completion via Iterative Soft-Thresholded SVD. Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an ”EM” flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named ”Incomplete” that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)

References in zbMATH (referenced in 63 articles , 2 standard articles )

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  1. Chen, Yuxin; Chi, Yuejie; Fan, Jianqing; Ma, Cong; Yan, Yuling: Noisy matrix completion: understanding statistical guarantees for convex relaxation via nonconvex optimization (2020)
  2. Kuang, Shenfen; Chao, Hongyang; Li, Qia: Majorized proximal alternating imputation for regularized rank constrained matrix completion (2020)
  3. Mazumder, Rahul; Saldana, Diego; Weng, Haolei: Matrix completion with nonconvex regularization: spectral operators and scalable algorithms (2020)
  4. Mazumder, Rahul; Weng, Haolei: Computing the degrees of freedom of rank-regularized estimators and cousins (2020)
  5. Robin, Geneviève; Klopp, Olga; Josse, Julie; Moulines, Éric; Tibshirani, Robert: Main effects and interactions in mixed and incomplete data frames (2020)
  6. Sportisse, Aude; Boyer, Claire; Josse, Julie: Imputation and low-rank estimation with missing not at random data (2020)
  7. Sun, Dengdi; Bao, Yuanyuan; Ge, Meiling; Ding, Zhuanlian; Luo, Bin: Dual-graph regularized sparse low-rank matrix recovery for tag refinement (2020)
  8. Yu, Guan; Li, Quefeng; Shen, Dinggang; Liu, Yufeng: Optimal sparse linear prediction for block-missing multi-modality data without imputation (2020)
  9. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  10. Kumar, Anil; Liang, Che-Yuan: Credit constraints and GDP growth: evidence from a natural experiment (2019)
  11. Kürschner, Patrick; Dolgov, Sergey; Harris, Kameron Decker; Benner, Peter: Greedy low-rank algorithm for spatial connectome regression (2019)
  12. Mao, Xiaojun; Chen, Song Xi; Wong, Raymond K. W.: Matrix completion with covariate information (2019)
  13. Matsuda, Takeru; Komaki, Fumiyasu: Empirical Bayes matrix completion (2019)
  14. Park, Seyoung; Zhao, Hongyu: Sparse principal component analysis with missing observations (2019)
  15. Rabin, Neta; Fishelov, Dalia: Two directional Laplacian pyramids with application to data imputation (2019)
  16. Wong, Raymond K. W.; Zhang, Xiaoke: Nonparametric operator-regularized covariance function estimation for functional data (2019)
  17. Amjad, Muhammad; Shah, Devavrat; Shen, Dennis: Robust synthetic control (2018)
  18. Bertsimas, Dimitris; Pawlowski, Colin; Zhuo, Ying Daisy: From predictive methods to missing data imputation: an optimization approach (2018)
  19. Cottet, Vincent; Alquier, Pierre: 1-bit matrix completion: PAC-Bayesian analysis of a variational approximation (2018)
  20. Fithian, William; Mazumder, Rahul: Flexible low-rank statistical modeling with missing data and side information (2018)

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