partition

Returns a list of two matrices <code>train</code> and <code>test</code>.
<code>train</code> corresponds to the input matrix with a fixed persentage of
entries masked by NA values. <code>test</code> is the complement of <code>train</code>
and contains the values of the input matrix in the cells where <code>train</code>
is NA, while all the other entries are filled by NA values.

internal

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).

Cristian Castiglione

sgdGMF

Estimation of Generalized Matrix Factorization Models via
Stochastic Gradient Descent

Davide Risso

Alexandre Segers

partition function

<dl><dt>y</dt>
<dd>input matrix to be split into train and test sets</dd>
<dt>p</dt>
<dd>fraction of observations to be used for the test set</dd></dl>

Arguments

Split the data matrix in train and test sets — partition

<dl>

<dt>y</dt>
<dd>input matrix to be split into train and test sets</dd>


<dt>p</dt>
<dd>fraction of observations to be used for the test set</dd>

</dl>

partition: Split the data matrix in train and test sets

Description

Usage

Arguments