deBias

<code>softImpute</code> uses shrinkage when completing a matrix with missing
values. This function debiases the singular values using ordinary least
squares.

multivariate

models

array

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).

Trevor Hastie

softImpute

Matrix Completion via Iterative Soft-Thresholded SVD

Rahul Mazumder

Balasubramanian Narasimhan

deBias function

<dl><dt>x</dt>
<dd>matrix with missing entries, or a matrix of class
<code>"Incomplete"</code></dd>
<dt>svdObject</dt>
<dd>an SVD object, the output of <code>softImpute</code></dd></dl>

Arguments

Trevor Hastie Maintainer: Trevor Hastie
<a href="/link/hastie%40stanford.edu?package=softImpute&version=1.4-3" data-mini-rdoc="softImpute::hastie@stanford.edu">hastie@stanford.edu</a>

Author

Recompute the $d component of a "softImpute" object through
regression. — deBias

<dl>

<dt>x</dt>
<dd>matrix with missing entries, or a matrix of class
<code>"Incomplete"</code></dd>


<dt>svdObject</dt>
<dd>an SVD object, the output of <code>softImpute</code></dd>

</dl>

Trevor Hastie Maintainer: Trevor Hastie
<a href='mailto:hastie@stanford.edu'>hastie@stanford.edu</a>

deBias: Recompute the `$d` component of a `"softImpute"` object through regression.

Description

Usage

Value

Arguments

Author

Details

Examples