Matrix Completion, Imputation, and Inpainting Methods
Randomly assign NAs to the data matrix with probability x
Matrix Completion by Universal Singular Value Thresholding
HardImpute : Generalized Spectral Regularization
Imputation using Weighted K-nearest Neighbors
Low-Rank Completion with Nuclear Norm Optimization
OptSpace
Imputation by Simple Rules
SoftImpute : Spectral Regularization
Iterative Regression against Right Singular Vectors
Singular Value Thresholding for Nuclear Norm Optimization
Matrix Completion, Imputation, and Inpainting Methods
Filling in the missing entries of a partially observed data is one of fundamental problems in various disciplines of mathematical science. For many cases, data at our interests have canonical form of matrix in that the problem is posed upon a matrix with missing values to fill in the entries under preset assumptions and models. We provide a collection of methods from multiple disciplines under Matrix Completion, Imputation, and Inpainting. See Davenport and Romberg (2016) <doi:10.1109/JSTSP.2016.2539100> for an overview of the topic.