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).


Reference manual

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1.4-1 by Trevor Hastie, 6 months ago

Browse source code at

Authors: Trevor Hastie <[email protected]> and Rahul Mazumder <[email protected]>

Documentation:   PDF Manual  

Task views: Missing Data

GPL-2 license

Depends on Matrix, methods

Suggests knitr, rmarkdown

Imported by NADIA, NIMAA, OmicsPLS, dbMC, gsbm, mashr, mimi, primePCA, tsensembler.

Depended on by ECLRMC.

See at CRAN