CMF - Collective Matrix Factorization
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.