CMF

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) doi:10.48550/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.

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