softImpute v1.4

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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)

Functions in softImpute

Name Description
Incomplete create a matrix of class Incomplete
deBias Recompute the $d component of a "softImpute" object through regression.
splr create a SparseplusLowRank object
svd.als compute a low rank soft-thresholded svd by alternating orthogonal ridge regression
complete make predictions from an svd object
Incomplete-class Class "Incomplete"
SparseplusLowRank-class Class "SparseplusLowRank"
lambda0 compute the smallest value for lambda such that softImpute(x,lambda) returns the zero solution.
softImpute impute missing values for a matrix via nuclear-norm regularization.
biScale standardize a matrix to have optionally row means zero and variances one, and/or column means zero and variances one.
softImpute-internal Internal softImpute functions
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Vignettes of softImpute

Name
softImpute.Rmd
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Details

Type Package
Date 2015-2-13
License GPL-2
VignetteBuilder knitr
LazyLoad yes
Packaged 2015-04-07 20:08:09 UTC; hastie
NeedsCompilation yes
Repository CRAN
Date/Publication 2015-04-08 00:42:55

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