Imputation by the nuclear-norm regularization — impute.nuc

Apply imputation to the data by the nuclear-norm regularization (Hastie et al. 2015) .

Usage

impute.nuc_norm(
  dataSet,
  rank.max = NULL,
  lambda = NULL,
  thresh = 1e-05,
  maxit = 100,
  final.svd = TRUE,
  seed = 362436069
)

Arguments

dataSet: A data frame containing the data signals.
rank.max: An integer specifying the restriction on the rank of the solution. The default is set to one less than the minimum dimension of the dataset.
lambda: A scalar specifying the nuclear-norm regularization parameter. If lambda = 0, the algorithm convergence is typically slower. The default is set to the maximum singular value obtained from the singular value decomposition (SVD) of the dataset.
thresh: A scalar (default = 1e-5) specifying the convergence threshold, measured as the relative change in the Frobenius norm between two successive estimates.
maxit: An integer (default = 100) specifying the maximum number of iterations before the convergence is reached.
final.svd: A boolean (default = TRUE) specifying whether to perform a one-step unregularized iteration at the final iteration, followed by soft-thresholding of the singular values, resulting in hard zeros.
seed: An integer (default = 362436069) specifying the seed used for the random number generator for reproducibility.

Value

The imputed data.

References

Hastie T, Mazumder R, Lee JD, Zadeh R (2015). “Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares.” Journal of Machine Learning Research, 16(104), 3367—3402. http://jmlr.org/papers/v16/hastie15a.html.