Matrix Completion via Iterative Soft-Thresholded SVD
standardize a matrix to have optionally row means zero and variances o...
make predictions from an svd object
Recompute the $d component of a "softImpute" object through regres...
Class "Incomplete"
create a matrix of class Incomplete
compute the smallest value for lambda such that `softImpute(x,lambda...
Internal softImpute functions
impute missing values for a matrix via nuclear-norm regularization.
Class "SparseplusLowRank"
create a SparseplusLowRank object
compute a low rank soft-thresholded svd by alternating orthogonal ridg...
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).