# 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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## Last month downloads

## 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 |

suggests | knitr |

depends | Matrix , methods |

Contributors | Trevor Hastie, Rahul Mazumder |

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