make.pos.diag

Fix sign ambiguity of eigen-vectors by making U positive diagonal

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

make.pos.diag function

<dl><dt>U</dt>
<dd>target matrix</dd></dl>

Arguments

Fix sign ambiguity of eigen-vectors — make.pos.diag

<dl>

<dt>U</dt>
<dd>target matrix</dd>

</dl>

make.pos.diag: Fix sign ambiguity of eigen-vectors

Description

Usage

Arguments