p_gradUsparseWrapper

Internal function for computing the gradients

Collective matrix factorization (CMF) finds joint low-rank
representations for a collection of matrices with shared row or column
entities. This code learns a variational Bayesian approximation for CMF,
supporting multiple likelihood potentials and missing data, while
identifying both factors shared by multiple matrices and factors private
for each matrix. For further details on the method see
Klami et al. (2014) <arXiv:1312.5921>.
The package can also be used to learn Bayesian canonical correlation
analysis (CCA) and group factor analysis (GFA) models, both of which are
special cases of CMF. This is likely to be useful for people looking for
CCA and GFA solutions supporting missing data and non-Gaussian likelihoods.
See Klami et al. (2013) <https://research.cs.aalto.fi/pml/online-papers/klami13a.pdf>
and	Virtanen et al. (2012) <http://proceedings.mlr.press/v22/virtanen12.html>
for details on Bayesian CCA and GFA, respectively.

Felix Held

Collective Matrix Factorization

Arto Klami

Lauri Väre

p_gradUsparseWrapper function

<dl><dt>r</dt>
<dd>?</dd>
<dt>par</dt>
<dd>?</dd>
<dt>stochastic</dt>
<dd>Whether or not to perform updates on a subsample</dd></dl>

Arguments

Internal function for computing the gradients — p_gradUsparseWrapper

<dl>

<dt>r</dt>
<dd>?</dd>


<dt>par</dt>
<dd>?</dd>


<dt>stochastic</dt>
<dd>Whether or not to perform updates on a subsample</dd>

</dl>

p_gradUsparseWrapper: Internal function for computing the gradients

Description

Usage

Value

Arguments