## softImpute: 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.

Version: |
1.0 |

Depends: |
Matrix, methods |

Published: |
2013-04-03 |

Author: |
Trevor Hastie and Rahul Mazumder |

Maintainer: |
Trevor Hastie <hastie at stanford.edu> |

License: |
GPL-2 |

NeedsCompilation: |
yes |

CRAN checks: |
softImpute results |

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