Iterative methods for matrix completion that use nuclearnorm regularization. There are two main approaches.The one approach uses iterative softthresholded 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 sparsematrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing lowrank SVDs on large sparse centered matrices (i.e. principal components)
Package details 


Author  Trevor Hastie <hastie@stanford.edu> and Rahul Mazumder <rahul.mazumder@gmail.com> 
Date of publication  20150408 00:42:55 
Maintainer  Trevor Hastie <hastie@stanford.edu> 
License  GPL2 
Version  1.4 
Package repository  View on CRAN 
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