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Collective matrix factorization (CMF) finds joint lowrank representations for a collection of matrices with shared row or column entities. This code learns 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.
Package details 


Author  Arto Klami and Lauri Väre 
Date of publication  20140325 14:26:42 
Maintainer  Arto Klami <arto.klami@cs.helsinki.fi> 
License  GPL (>= 2) 
Version  1.0 
Package repository  View on CRAN 
Installation 
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