optimal_SVHT_coef

A function for the calculation of the coefficient determining optimal
location of hard threshold for matrix denoising by singular values hard thresholding
when noise level is known or unknown. Recreation of MATLAB code by Matan Gavish and
David Donoho.

Data whitening is a widely used preprocessing step to remove correlation structure since statistical models often assume independence. Here we use a probabilistic model of the observed data to apply a whitening transformation. This Gaussian Inverse Wishart Empirical Bayes model substantially reduces computational complexity, and regularizes the eigen-values of the sample covariance matrix to improve out-of-sample performance.

Gabriel E Hoffman

decorrelate

Decorrelation Projection Scalable to High Dimensional Data

Gabriel Hoffman

optimal_SVHT_coef function

<dl><dt>beta</dt>
<dd>A single value or a vector that represents aspect ratio m/n of the matrix to
be denoised. 0&lt;<code>beta</code>&lt;=1.</dd>
<dt>sigma_known</dt>
<dd>A logical value. TRUE if noise level known, FALSE if unknown.</dd></dl>

Arguments

Optimal Hard Threshold for Singular Values — optimal_SVHT_coef

<dl>

<dt>beta</dt>
<dd>A single value or a vector that represents aspect ratio m/n of the matrix to
be denoised. 0&lt;<code>beta</code>&lt;=1.</dd>


<dt>sigma_known</dt>
<dd>A logical value. TRUE if noise level known, FALSE if unknown.</dd>

</dl>

optimal_SVHT_coef: Optimal Hard Threshold for Singular Values

Description

Usage

Value

Arguments

References